Search Results (317)

This study developed an automated multistage countercurrent extraction device and applied it to the separation and extraction of phenolic acids—including neochlorogenic acid, chlorogenic acid, cryptochlorogenic acid, isochlorogenic acid A, isochlorogenic acid B, and isochlorogenic acid C—from an aqueous extract of Lonicera japonica Thunb. The extraction process was optimized by systematically evaluating critical parameters such as liquid–liquid equilibrium pH, internal diameter of the tee connector, phase flow rate ratio, and the number of extraction stages. The apparent partition coefficients of all six phenolic acids increased with decreasing aqueous pH, with fitted pKa values ranging from 3.7 to 4.3. A reduction in tee diameter (0.75 mm) was found to enhance mass transfer efficiency. Increasing the flowrate of both phases (20 mL/min), the organic-to-aqueous phase ratio (4:1), and the number of extraction stages (3 stages) significantly improved both stage efficiency and overall extraction yield. Under optimized conditions, the target chlorogenic acids were efficiently enriched, with their total content increasing from 50.3 mg/g to 70.1 mg/g in the solid residue after three countercurrent stages. The automated multistage countercurrent extraction system demonstrated robust performance, suggesting promising potential for applications in the preparation of traditional Chinese medicine ingredients or as an automated sample pretreatment method in analytical workflows. This study provides a novel and green technological solution for efficient separation of complex TCM systems. Full article

Background: Comprehensive data on the economic burden of cervical cancer treatment remain scarce in China’s less developed regions, necessitating this study on hospitalization costs and expenditure trends in these areas. Methods: Employing a multi-stage stratified cluster sampling approach, this study enrolled 10,070 cervical cancer inpatients from 72 healthcare facilities in Gansu Province. Clinical and expenditure data were extracted from hospital information systems. Rank sum tests and Spearman correlation analyses were performed for univariate assessment, while quantile regression and random forest models were applied to identify determinant factors. Results: From 2019 to 2023, the average hospitalization duration for cervical cancer patients in Gansu Province was 16.12 days, with an average hospitalization cost of USD 3862.08 (2023 constant prices, converted from CNY at 1:7.0467). During these five years, the average inpatient costs per hospitalization increased from USD 3473.45 to USD 4202.57, and the average daily hospitalization cost rose from USD 230.53 to USD 241.77. The average drug cost decreased from USD 769.06 to USD 640.16. The main factors influencing hospitalization costs included the length of hospital stay, whether cervical cancer surgery was performed, hospital type, hospital level, and the proportion of medications. Conclusions: Our findings indicate that cervical cancer is a considerable economic burden on both families and society. This highlights the need to control the length of hospital stay and optimize the allocation of medical resources, in addition to strengthening cervical cancer screening and HPV vaccination in underdeveloped areas, in order to enhance the efficiency of prevention and treatment and ensure medical equity. Full article

Weld seam detection is a fundamental prerequisite for robotic welding automation, yet it remains challenging due to the elongated shape of welds, weak contrast against metallic backgrounds, and significant environmental interference in industrial scenarios. To address these challenges, we propose a novel deep neural network architecture termed SANet (Strip-Aware Network). The model is constructed upon a U-shaped backbone and integrates strip-aware feature modeling with multistage supervision. It mainly consists of two complementary modules: the Paralleled Strip and Spatial Context-Aware (PSSCA) module and the Multistage Fusion (MF) module. The PSSCA module enhances the extraction of elongated strip-like features by combining parallel strip perception with spatial context modeling, thereby improving fine-grained weld seam representation. In addition, SANet integrates the StripPooling attention mechanism as an auxiliary component to enlarge the receptive field along strip directions and enhance feature discrimination under complex backgrounds. Meanwhile, the MF module performs cross-stage feature fusion by aggregating encoder and decoder features at multiple levels, ensuring accurate boundary recovery and robust global-to-local interaction. The weld seam detection task is formulated as a two-dimensional segmentation problem and evaluated on a self-built dataset consisting of over 4000 weld seam images covering diverse industrial scenarios such as pipe joints, trusses, elbows, and furnace structures. Experimental results show that SANet achieves an IoU of 96.23% and a Dice coefficient of 98.07%, surpassing all compared models and demonstrating its superior performance in weld seam detection. These findings validate the effectiveness of the proposed architecture and highlight its potential as a low-cost, flexible, and reliable pure vision solution for intelligent welding applications. Full article

Time and frequency standards constitute fundamental requirements for diverse applications spanning daily life technologies to advanced scientific research. Among precision time dissemination methods, microwave-clock-based dual comb time transfer has emerged as a promising approach that achieves ultra-precise time interval measurements through linear optical sampling. However, conventional peak detection methodologies employed in such systems exhibit critical limitations: vulnerability to amplitude noise interference and inherent accuracy constraints imposed by analog sampling rates. To address these challenges, we present a novel digital time differential measurement paradigm integrating three key algorithmic innovations: (1) adaptive signal detection and extraction protocols, (2) multi-stage noise suppression processing, and (3) optimized centroid determination techniques. This comprehensive digital processing framework significantly enhances both measurement stability and operational efficiency, demonstrating single-shot temporal resolution at 17.6 fs stability levels. Our method establishes new capabilities for high-precision time-frequency transfer applications requiring robust noise immunity and enhanced sampling dynamics. Full article

Triboelectric nanogenerators (TENGs) have emerged as efficient mechanical-energy harvesters with advantages—simple architectures, broad material compatibility, low cost, and strong environmental tolerance—positioning them as key enablers of self-powered systems. This review synthesizes recent progress in energy-storage interfaces, power management, and system-level integration for TENGs. We analyze how intrinsic source characteristics—high output voltage, low current, large internal impedance, and pulsed waveforms—complicate efficient charge extraction and utilization. Accordingly, this work highlights a variety of power-conditioning approaches, including advanced rectification, multistage buffering, impedance transformation/matching, and voltage regulation. Moreover, recent developments in the integration of TENGs with storage elements, cover hybrid topologies and flexible architectures. Application case studies in wearable electronics, environmental monitoring, smart-home security, and human–machine interfaces illustrate the dual roles of TENGs as power sources and self-driven sensors. Finally, we outline research priorities: miniaturized and integrated power-management circuits, AI-assisted adaptive control, multimodal hybrid storage platforms, load-adaptive power delivery, and flexible, biocompatible encapsulation. Overall, this review provides a consolidated view of state-of-the-art TENG-based self-powered systems and practical guidance toward real-world deployment. Full article

In autonomous-driving real-time semantic segmentation, simultaneously maximizing accuracy, minimizing model size, and sustaining high inference speed remains challenging. This tripartite demand poses significant constraints on the design of lightweight neural networks, as conventional frameworks often suffer from a trade-off between computational efficiency and feature representation capability, thereby limiting their practical deployment in resource-constrained autonomous driving systems. We introduce ISANet, an information supplementary aggregation framework that markedly elevates segmentation accuracy without sacrificing frame rate. ISANet integrates three key components: (i) the Spatial-Supplementary Lightweight Bottleneck Unit (SLBU) that splits channels and employs compensatory branches to extract highly expressive features with minimal parameters; (ii) the Missing Spatial Information Recovery Branch (MSIRB) that recovers spatial details lost during feature extraction; and (iii) the Object Boundary Feature Attention Module (OBFAM) that fuses multi-stage features and strengthens inter-layer information interaction. Evaluated on Cityscapes and CamVid, ISANet attains 76.7% and 73.8% mIoU, respectively, while delivering 58 FPS and 90 FPS with only 1.37 million parameters. Full article

Underreamed anchor bolts, as an emerging anchoring element in geotechnical engineering, operate via a fundamentally distinct load transfer mechanism compared with conventional friction type anchors. The accurate and reliable prediction of their ultimate bearing capacity constitutes a pivotal technological impediment to their broader engineering adoption. Firstly, this paper systematically elucidates the constituent mechanisms of underreamed anchor resistance and their progressive load transfer trajectory. Subsequently, in situ full-scale pull-out experiments are leveraged to decompose the load–displacement response throughout its entire evolution. The multi-stage development law and the underlying mechanisms governing the evolution of anchorage characteristics are thereby elucidated. Based on the experimental dataset, a three-dimensional elasto-plastic numerical model is rigorously established. The model delineates, at high resolution, the failure mechanism of surrounding soil mass and the spatiotemporal evolution of its three-dimensional displacement field. A definitive critical displacement criterion for the attainment of the ultimate bearing capacity of underreamed anchors is established. Consequently, analytical models for the ultimate side frictional stress and end-bearing capacity at the limit state are advanced, effectively circumventing the parametric uncertainties inherent in extant empirical formulations. Ultimately, characteristic parameters of the elasto-plastic branch of the load–displacement curve are extracted. An ultimate bearing capacity prognostic framework, founded on an optimized hyperbolic model, is established. Its superior calibration fidelity to the evolving load–displacement response and its demonstrable engineering applicability are rigorously substantiated. Full article

To meet the increasing demands of energy efficiency and real-time performance in autonomous driving systems, this paper presents a lightweight and robust LiDAR SLAM framework designed with power-aware considerations. The proposed system introduces three core innovations. First, it replaces traditional ordered point cloud indexing with a 2D range image projection, significantly reducing memory usage and enabling efficient feature extraction with curvature-based criteria. Second, a multi-stage outlier rejection mechanism is employed to enhance feature robustness by adaptively filtering occluded and noisy points. Third, we propose a dynamically filtered local mapping strategy that adjusts keyframe density in real time, ensuring geometric constraint sufficiency while minimizing redundant computation. These components collectively contribute to a SLAM system that achieves high localization accuracy with reduced computational load and energy consumption. Experimental results on representative autonomous driving datasets demonstrate that our method outperforms existing approaches in both efficiency and robustness, making it well-suited for deployment in low-power and real-time scenarios within intelligent transportation systems. Full article

Despite the long history of beech (Fagus sylvatica L.) red heartwood research, there has been no experimental proof on the structure of the chromophores yet. For the first time, using high-performance liquid chromatography/diode array detection/multistage electrospray ionization mass spectrometry, it was evidenced that red heartwood chromophores are water/methanol solvent extractable high molecular weight (400–2200 Da) compounds, which are polymerized, transformed, and oxidized products of (epi)catechin and taxifolin. Acid soluble non-extractable polyphenols (flavonoids, tannins) were not evidenced in the cell wall structure, while alkaline soluble compounds (ferulic acid, dehydrodiferulic acid, p-coumaric acid) have been identified for the first time from the sapwood/red heartwood boundary tissues: these supposedly play a role in the structural reinforcement of the cell wall structure and in the antioxidant protection and have a lesser role in color formation. Results on the structure of chromophores and on cell wall composition may enhance color homogenization technologies and contribute to a better utilization of red-heartwooded timber in the future. Full article

Amid the increasing frequency and severity of major disasters, the rapid spread of online misinformation poses substantial risks to public safety, effective crisis management, and long-term societal sustainability. Current methods for managing disaster-related rumors rely on static, rule-based approaches that lack scalability, fail to capture nuanced misinformation, and are limited to reactive responses, hindering effective disaster management. To address this gap, this study proposes a novel framework that leverages large language models (LLMs) and event knowledge graphs (EKGs) to facilitate the sustainable agile identification and adaptive control of disaster-related online rumors. The framework follows a multi-stage process, which includes the collection and preprocessing of disaster-related online data, the application of Gaussian Mixture Wasserstein Autoencoders (GMWAEs) for sentiment and rumor analysis, and the development of EKGs to enrich the understanding and reasoning of disaster events. Additionally, an enhanced model for rumor identification and risk control is introduced, utilizing Graph Attention Networks (GATs) to extract node features for accurate rumor detection and prediction of rumor propagation paths. Extensive experimental validation confirms the efficacy of the proposed methodology in improving disaster response. This study contributes novel theoretical insights and presents practical, scalable solutions for rumor control and risk management during crises. Full article

Background/objectives: Multi-ligament knee injuries (MLKIs) present complex surgical challenges, and there remains no consensus on whether single-stage or staged reconstruction yields superior outcomes. This study aimed to assess differences in complications, functional outcomes, and return-to-sport rates between single-stage and staged surgical approaches. Materials and Methods: A systematic review was conducted in accordance with PRISMA guidelines. Four databases (PubMed, Scopus, Embase, and the Cochrane Library) were searched for studies published between 2000 and 2025. Eligible studies reported surgical management of MLKIs and specified either single-stage or multi-stage reconstruction. Data on complications, functional scores (Lysholm), return to sport, rehabilitation protocols, and graft type were extracted and analyzed using descriptive statistics and study-level regression models. Results: A total of 43 studies encompassing 2086 patients were included (1900 single-stage; 186 multi-stage). Staged reconstruction was associated with a significantly lower rate of arthrofibrosis (1.95% vs. 7.29%; OR 3.96, p = 0.007), higher Lysholm scores (+4.7 points, p < 0.001), and higher return-to-sport rates (48% vs. 65%, p = 0.001) compared to single-stage. Use of synthetic grafts increased the risk of arthrofibrosis (OR 4.09, p = 0.031). Early mobilization and weightbearing were not associated with increased arthrofibrosis risk. Conclusions: Staged reconstruction may yield better functional outcomes and lower complication rates—particularly arthrofibrosis, compared to single-stage approaches. These findings support an individualized surgical strategy, guided by injury complexity, graft selection, rehabilitation goals, and patient-specific functional demands. Full article

This systematic literature review addresses the critical challenge of ensuring robustness and adaptability in AI-based machine health monitoring (MHM) systems. While the field has seen a surge in research, a significant gap exists in understanding how to effectively manage data scarcity, unknown fault types, and the integration of diverse data streams for real-world industrial applications. The problem is magnified by the rarity of failure events, which leads to imbalanced datasets and hampers the generalizability of predictive models. To synthesize the current state of research and identify key solutions, we followed a rigorous, modified PRISMA methodology. A comprehensive search across Scopus, IEEE Xplore, Web of Science, and Litmaps initially yielded 3235 records. After a multi-stage screening process, a final corpus of 85 peer-reviewed studies was selected. Data were extracted and synthesized based on a thematic framework of 13 core research questions. A bibliometric analysis was also conducted to quantify publication trends and research focus areas. The analysis reveals a rapid increase in research, with publications growing from 1 in 2018 to 35 in 2025. Key findings highlight the adoption of transfer learning and generative AI to combat data scarcity, with multimodal data fusion emerging as a crucial strategy for enhancing diagnostic accuracy. The most active research themes were found to be Predictive Maintenance and Edge Computing, with 12 and 10 references, respectively, while critical areas like standardization remain under-explored. Overall, this review shows that AI benefits machine health monitoring but still faces challenges in reproducibility, benchmarking, and large-scale validation. Its main limitation is the focus on English peer-reviewed studies, excluding industry reports and non-English work. Future research should develop standardized datasets, energy-efficient edge AI, and socio-technical frameworks for trust and transparency. The study offers a structured overview, a roadmap for future work, and underscores the importance of AI in Industry 4.0. Full article

Background/Objectives: Marine-derived oils rich in long-chain polyunsaturated fats have long been associated with positive effects on plasma lipid levels and anti-inflammatory responses. Abalone viscera are rich in oils that are rarely extracted and made available. Methods: Abalone viscera oil (AVO) was extracted by multistage countercurrent extraction using ethanol as a solvent, and its oil quality, fatty acid composition, and in vitro antioxidant activity were determined. Meanwhile, the anti-hyperlipidemic effect of AVO on HFD-induced hyperlipidemia mice was evaluated. Results: The abalone viscera were extracted at a solid–liquid ratio of 1:3 with an oscillation frequency of 300 rpm for 40 min, and the extraction rate was 81.18% after four-stage countercurrent extraction. The acid value, iodine value, peroxide value, vitamin E, and astaxanthin of AVO were 1.26 mg KOH/g, 140.9 g/100 g, 3.6 meq/kg, 105 mg/kg, and 533.8 mg/kg, respectively. The unsaturated fatty acids of AVO account for 56.60%, with eicosapentaenoic acid (C20:5n3) and arachidonic acid (C20:4n6) the two predominant PUFAs, and oleic acid (C18:1n9) the most dominant MUFA. The DPPH, ABTS, and ·OH radicals scavenging capacities of AVO increased with concentration, and the IC₅₀ values were 6.30 mg/mL, 0.45 mg/mL, and 8.95 mg/mL, respectively. Moreover, the administration of AVO significantly alleviated HFD-induced weight gain, liver fat accumulation, lipid disorder, and oxidative stress in mice. Conclusions: Collectively, our study provides a theoretical basis for the application of AVO and the comprehensive utilization of abalone viscera, which helps increase the additional value of abalone. Full article

In low-light environments, light field (LF) images are often affected by various degradation factors, which impair the performance of subsequent visual tasks such as depth estimation. To address these challenges, although numerous light-field low-light enhancement methods have been proposed, they generally overlook the importance of frequency-domain information in modeling light field features, thereby limiting their noise suppression capabilities. Moreover, these enhancement methods mainly rely on pixel- or semantic-level cues without explicitly incorporating disparity information for structural modeling, thereby overlooking the stereoscopic spatial structure of light field images and limiting enhancement performance across different depth levels. To address these issues, we propose a light field low-light enhancement method named DDPNet. The method integrates a depth-guided mechanism to jointly restore light field images in both the spatial and frequency domains, employing a multi-stage progressive strategy to achieve synergistic improvements in illumination and depth. Specifically, we introduce a Dual-Domain Feature Extraction (DDFE) module, which incorporates spatial-frequency analysis to efficiently extract both global and local light field features. In addition, we propose a Depth-Aware Enhancement (DAE) module, which utilizes depth maps to guide the enhancement process, effectively restoring edge structures and luminance information. Extensive experimental results demonstrate that DDPNet significantly outperforms existing methods. Full article

Artificial intelligence (AI) adoption in strategic sectors such as energy is often framed in optimistic narratives, yet its actual economic contribution remains under-quantified. This study proposes a novel, multi-stage methodology at the intersection of machine learning, statistics, and big data analytics to bridge this gap. First, we construct a media-derived AI Adoption Score using natural language processing (NLP) techniques, including dictionary-based keyword extraction, sentiment analysis, and zero-shot classification, applied to a large corpus of firm-related news and scientific publications. To enhance reliability, we introduce a Misinformation Bias Score (MBS)—developed via zero-shot classification and named entity recognition—to penalise speculative or biased reporting, yielding a credibility-adjusted adoption metric. Using these scores, we classify firms and apply a Fixed Effects Difference-in-Differences (FE DiD) econometric model to estimate the causal effect of AI adoption on turnover. Finally, we scale firm-level results to the macroeconomic level via a Leontief Input–Output model, quantifying direct, indirect, and induced contributions to GDP and employment. Results show that AI adoption in Romania’s energy sector accounts for up to 42.8% of adopter turnover, contributing 3.54% to national GDP in 2023 and yielding a net employment gain of over 65,000 jobs, despite direct labour displacement. By integrating machine learning-based text analytics, statistical causal inference, and big data-driven macroeconomic modelling, this study delivers a replicable framework for measuring credible AI adoption and its economy-wide impacts, offering valuable insights for policymakers and researchers in digital transformation, energy economics, and sustainable development. Full article