Search Results (2,371)

Sea buckthorn is rich in functional components but features high acidity and susceptibility to oxidative deterioration, leading to flavor defects such as sour–astringent taste and rancidity in single-fruit wine. Co-fermentation is an effective strategy for flavor and nutrition complementarity, but the dynamic evolution of volatile aroma during yeast–lactic acid bacteria combined fermentation of such wine remains unclear. This study employed ‘Zhuangyuan Huang’ sea buckthorn and ‘Marselan’ grape to produce the composite fruit wine via sequential co-fermentation, and systematically investigated dynamic aroma changes using HS-SPME-GC-MS, orthogonal partial least squares discriminant analysis (OPLS-DA), and relative odor activity value (ROAV) analysis. Results revealed that post-fermentation, total detected volatile compounds increased from 90 to 118, with esters and alcohols rising by 24 and 11 respectively, serving as core contributors to enhanced aroma richness. ROAV analysis demonstrated this process significantly reduced the contribution of off-flavor acids, boosted the importance of floral, fruity, and sweet compounds, and elevated the sensory score from 26.8 to 84.1. OPLS-DA further confirmed significant inter-stage aroma differences with excellent intergroup discrimination. These findings confirm that this sequential fermentation breaks processing bottlenecks of high-acid fruits, reveals the synergistic flavor-modulating effects of multi-microbial sequential fermentation, and provides theoretical support for process optimization and high-value processing of composite fruit wine. Full article

Quantum dots (QDs) are semiconductor nanocrystals with unique photophysical properties, rendering them promising for applications in biomedical imaging, neuroscience, and various industrial sectors. However, the rapid expansion of their production and application inevitably leads to the release of QDs into the environment throughout their life cycle, classifying them as an emerging class of contaminants of concern. Their potential neurotoxicity not only represents a major bottleneck obstructing their clinical translation but also poses environmental and health risks that warrant serious attention. This review summarizes recent advances in the neurotoxicity of QDs, with a focus on their adverse effects on the central and peripheral nervous systems. It indicates that the mechanisms of QD neurotoxicity involve a complex network comprising oxidative stress, metabolic reprogramming, neuroinflammation, and multiple cell death pathways. Notably, the peripheral nervous system is highlighted as an early-warning target, and the significant risks associated with long-term, low-dose environmental exposure are emphasized. Full article

Robust flame stabilization in low-Mach, low-enthalpy supersonic combustors is a core bottleneck for turbine-based combined cycle (TBCC) mode transition. Existing studies mainly focus on single-injector configurations, while the injection angle modulation mechanism for multi-injector cavity flameholders remains unclear under TBCC-relevant conditions. This work experimentally investigated the effects of 30°, 45°, and 90° injection angles on cold-flow mixing, reacting flow topology, and flame stabilization in a Mach 1.6, 660 K dual-injector cavity combustor. Results showed that the overall cold-flow jet penetration capacity in the fully developed far field increased with injection angle following the order of 90° > 45° > 30°. Combustion heat release universally enhanced jet penetration, with a maximum 25% augmentation observed at 30° injection, which attenuated with steepening injection angle. Moreover, flame stability exhibited a non-monotonic trend in the tested dual-injector configuration. Full article

The global shortage of high-quality protein feed resources continues to widen, and the development of high-value-added woody plants is a key strategy for alleviating feed shortage. The mulberry (Morus alba L.) is a recognized high-protein woody forage resource. However, the inconsistent quality of its natural silage and the unclear risk of mycotoxins represent the core bottlenecks limiting its widespread adoption as feed. Four treatments were set up in this study: (1) control; (2) lactic acid bacteria inoculant (LAB, Lactiplantibacillus plantarum); (3) cellulase enzyme (AC, Acremonium cellulolyticum); (4) a mixture of LAB + AC. After 60 days of ensiling, a systematic analysis was conducted to examine the effects of exogenous microbial inoculant and enzyme preparation on the fermentation quality, bacterial community, and mycotoxin in mulberry silages. Fresh mulberry exhibited a high crude protein content of 23% on a dry matter (DM) basis, making it a high-quality feed resource. Compared to the control, the addition of LAB and AC either alone or in combination, significantly improved (p < 0.001) the fermentation quality and safety of silages: lactic acid content increased from 0.85% DM to 1.41–2.03% DM; pH, ammonia nitrogen, and deoxynivalenol decreased from 4.85, 0.88% DM, and 3.92 μg/kg to 3.53–3.95, 0.40–0.55% DM, and 1.21–3.04 μg/kg, respectively. The combined LAB and AC treatment resulted in the most favorable fermentation performance of mulberry silage. Bacterial community analysis revealed that fresh mulberry exhibited high bacterial alpha diversity, with Gram-negative bacteria as the dominant bacterial community, and Sphingomonas roseiflava as a representative dominant species. After ensiling, bacterial alpha diversity decreased in all the silages. Furthermore, the Lactiplantibacillus plantarum eventually prevailed as the dominant bacteria and exhibiting the highest relative abundance in the LAB + AC-treated silage (57.23%). Bugbase functional prediction indicated that the proportion of potential pathogenic bacteria was significantly higher (p < 0.05) in fresh mulberry than silage. Thus, the synergistic action of LAB + AC treatment effectively optimized the ensiling fermentation process. Full article

Blockchain-assisted off-chain storage for IoT must simultaneously manage low-latency tiered data placement, trusted and dynamic on-chain mapping, migration consistency, and failure recovery—four concerns that existing designs address in isolation. Tiered storage systems optimize placement without modeling the scalable coordination cost of keeping object–location bindings trustworthy, while blockchain-metadata studies assume static storage topologies with no dynamic tier migration. This paper presents a scalable and trusted metadata-coordinated tiered off-chain storage framework, which bridges traditional trust systems (e.g., legacy authentication) with blockchain networks powered by Proof of Capacity (PoC) consensus. In this framework, adaptive heat-driven placement, dynamic on-chain mapping evolution with batched commitment, migration-aware redirect control, and rollback-safe recovery operate as a single coordinated workflow, with the five-stage write–verify–commit–redirect–retire pipeline acting as a lightweight coordination protocol that maintains ordered and atomic state transitions under message loss, out-of-order delivery, and single-node failures. The distinctive contribution lies in the framework’s coupled control: every placement decision propagates through a verifiable metadata path that can be audited and, when necessary, rolled back. Simulation across multiple workload patterns shows that the proposed method reduces average access latency by 28% and raises the hot-tier hit ratio from 0.19 to 0.65 relative to a dynamic baseline without trusted mapping coordination under the simulated registry write cost. To achieve high-throughput mapping operations, batched on-chain commitment cuts metadata transactions by 50× at the cost of a tunable mapping freshness delay. The framework scales from 1 k to 50 k managed objects, effectively managing tens of millions of bytes of data (10+ MB scale) without disproportionate overhead growth; beyond this scale, hot-tier capacity rather than coordination becomes the dominant bottleneck, and smarter predictive placement becomes the natural next lever. All tested fault types achieve 100% rollback success with sub-millisecond local data plane interruption; audit-visible recovery depends on the assumed chain finality delay and, for heavily regulated IoT domains, such as finance and healthcare, should be treated as the operationally binding recovery time objective. These results, together with extended evaluations—including asymmetric write latency stress, coordination ablation, tail latency analysis, and benefit–complexity assessment—provide quantitative evidence that scalable, dynamic mapping coordination can be integrated into tiered off-chain data management at an acceptable and measurable operational cost under the simulated configuration. Full article

Keratoconus, a progressive corneal ectasia, remains a major cause of irreversible visual impairment worldwide. Conventional corneal cross-linking (CXL) with riboflavin/ultraviolet A (UVA) has revolutionized clinical management, yet its efficacy is still constrained by epithelial barriers, oxygen dependence, and safety concerns in thin corneas. Emerging nanotechnology provides a transformative opportunity to overcome these bottlenecks. This review highlights the enhancement of riboflavin delivery efficiency by nanocarriers, the photodynamic optimization of nano-enhanced cross-linking agents, and the synergistic strengthening effect of nanocomposites on corneal mechanical strength. We emphasize not only their potential to enhance drug penetration, improve cross-linking efficiency, and extend clinical indications, but also their role in advancing toward a new generation of personalized, intelligent, and minimally invasive corneal therapy. Finally, we discuss translational challenges, including manufacturing, long-term biosafety, and regulatory frameworks, and present a theoretical roadmap that integrates nanotechnology, real-time imaging, and artificial intelligence (AI)-assisted decision-making to achieve a closed-loop “sense–decide–act” therapeutic system. By situating nanomaterial-enhanced CXL within precision ophthalmology, this review highlights its capacity to redefine the standard of care for keratoconus and related ectatic disorders. Full article

Long and narrow shielded confined spaces, represented by traffic tunnels and underground utility tunnels, constitute critical application scenarios for indoor and underground positioning services. Despite their relatively simple geometric configurations, such environments suffer from severe spatial distortion of geometric dilution of precision (GDOP). Coupled with pervasive low-elevation signal propagation and intensive multipath reflection effects, conventional BeiDou Navigation Satellite System (BDS) positioning services are unable to provide continuous and reliable coverage in these scenarios. To date, existing research on high-precision pseudolite positioning for narrow confined spaces remains largely confined to theoretical analysis and laboratory experimental verification, while systematic studies on application-oriented signal atlas feature network design are significantly insufficient, forming a prominent gap that restricts the practical engineering deployment of relevant technologies. To address the aforementioned technical bottlenecks, this paper proposes a novel BDS pseudolite signal atlas network design method to improve the continuity, stability and comprehensive positioning performance in spatially distorted narrow shielded environments. Field vehicular tests were carried out in actual engineering tunnels and underground utility tunnels to systematically analyze the variation characteristics of raw BDS pseudolite observation data, including pseudorange, carrier phase, carrier-to-noise ratio (C/N₀) and Doppler shift. The test results verified that kinematic Doppler parameters exhibited outstanding stability in complex shielded environments with strong multipath interference. On this basis, a spatial feature model based on kinematic Doppler measurements was constructed, and wavelet denoising technology was adopted to extract effective typical spatial feature parameters. Combined with the deterministic one-to-one mapping relationship between Doppler peak characteristics and spatial positions, a multi-peak kinematic Doppler atlas was established, which eliminates the dependence on pre-deployment data collection, dedicated database construction and offline model training. Furthermore, comprehensively considering multi-dimensional constraints such as spatial environment scale, carrier dynamic characteristics and terminal output rate, the atlas network scheme was optimized to achieve a balanced trade-off among positioning detection accuracy, absolute positioning precision and suppression of the pseudolite near-far effect. Comparative experimental results demonstrate that the proposed BDS pseudolite atlas network effectively resolves the inherent GNSS positioning difficulty in long and narrow shielded spaces. Benefiting from the rational spectral peak configuration strategy, the system can satisfy the continuous and stable positioning requirements of multiple carrier types including motor vehicles and railway locomotives under variable motion speeds and terminal output rates. This study provides a robust and feasible technical solution for high-precision BDS positioning services in long and narrow shielded confined spaces, and holds favorable engineering application prospects for underground navigation scenarios. Full article

Predicting the evolution of microstructure and field quantities under varying processing and loading conditions is a central challenge in computational materials science and metal additive manufacturing (AM). While deep learning (DL) methods offer ultra-fast prediction capabilities post-training, existing models often struggle with poor spatial and temporal extrapolation, high parameter burdens, and an inability to effectively integrate diverse conditioning parameters alongside high-dimensional input fields. To address these bottlenecks, we propose a novel conditional generative adversarial network (CPGAN), which is designed to seamlessly ingest both initial fields and governing condition parameters. The CPGAN framework offers three distinct advantages: (1) it accurately maps the combined effects of initial states and process conditions onto evolved fields; (2) it demonstrates robust extrapolation capabilities across diverse spatial and temporal scales, including the unique ability to natively generate high-resolution rectangular domains; and (3) it achieves superior predictive accuracy and training stability compared to standard convolutional baselines by effectively suppressing spurious artifacts. We validate CPGAN’s performance against rigorous physics-based ground truths across three representative engineering applications: porosity evolution in selective laser sintering (SLS), spatial distribution of 2D von Mises stress fields in solid structures, and the spatiotemporal evolution of grain growth. The results confirm that CPGAN is a highly adaptable and efficient surrogate model, capable of simulating continuous structural and morphological evolutions even when driven by highly non-uniform spatial or temporal kinetics. Full article

Accurate polyp segmentation in colonoscopic images remains challenging due to low contrast, irregular morphology, and significant distribution shifts across datasets, which often lead to unreliable boundary delineation and poor generalization. Existing methods typically treat boundary information as an auxiliary cue or incorporate boundary information through hand-crafted architectural designs, resulting in limited integration between boundary-sensitive features and region-aware representations. In this paper, we propose a boundary-aware multi-task learning framework, termed MBRSNet, which explicitly models and exploits the complementarity between the segmentation task and the auxiliary signed distance field (SDF) regression task. Specifically, we formulate boundary modeling as an auxiliary SDF regression task, providing dense and continuous structural supervision without requiring additional annotations. To effectively couple the two tasks, we design a cross-gated multi-task bottleneck that enables bidirectional and selective feature interaction, allowing each task to selectively leverage complementary information while suppressing task-irrelevant responses. Furthermore, a hierarchical cross-task guidance strategy is introduced in the decoding stage, where boundary-aware weighting and segmentation-guided alignment jointly refine multi-scale features, ensuring consistent integration of boundary cues and regional semantics. Extensive experiments on five benchmark datasets demonstrate that MBRSNet achieves competitive or superior performance compared with representative state-of-the-art methods in both segmentation accuracy and cross-dataset generalization. In particular, the proposed framework achieves superior boundary delineation under challenging conditions and exhibits strong robustness to domain shifts, highlighting the effectiveness of structured task interaction for boundary-aware medical image segmentation. Full article

Ulcerative colitis (UC) is a chronic inflammatory bowel disease characterized by a complex etiology and a protracted disease course. Active ingredients from traditional Chinese medicine (TCM), by leveraging the holistic regulatory advantages of anti-inflammatory activity, immune barrier preservation, and gut microbiota regulation, have shown unique therapeutic potential in the intervention of UC. Although bottlenecks such as unclear targets, fragmented mechanisms of action, and poor druggability constrain the clinical translation of TCM active ingredients, current research efforts are dedicated to overcoming these obstacles. This article reviews the latest research progress (2021–2026) on TCM active ingredients for UC treatment. It analyzes the anti-UC mechanisms from three core dimensions: chemical diversity and pharmacodynamic characteristics, validation of direct targets, and indirect regulation through the “gut microbiota–metabolite” axis. Moreover, it emphasizes recent breakthroughs in druggability optimization technologies, including carrier-based nano drug delivery systems (NDDS), carrier-free NDDS, co-delivery NDDS, and prodrug design strategy. Research demonstrates that TCM active ingredients achieve therapeutic effects by modulating inflammatory signaling networks, restoring intestinal immune homeostasis, repairing the mucosal barrier, and remodeling the gut microenvironment. Simultaneously, the application of novel delivery strategies effectively resolves issues such as poor solubility, low oral bioavailability, and insufficient colon targeting. Finally, this review suggests that future research on TCM active ingredients for UC therapy should concentrate on systematically clarifying multi-level mechanisms and designing clinically translatable smart drug delivery strategies, aiming to provide a theoretical basis and practical reference for promoting TCM modernization and innovative UC drug development. Full article

The pathological mechanism of autism spectrum disorder (ASD) exhibits dual heterogeneity: abnormal local energy metabolism and brain-wide high-order topological failure. To synergistically characterize these complex signals captured by advanced neuroimaging sensors, we propose the Quantum-Enhanced Graph Convolutional Wide Residual Network (QG-WRN), a modality-specific, decoupled parallel dual-stream architecture. In the classical branch, to accurately capture the spatial distribution of local metabolic abnormalities, we employ a wide residual network (WRN) to extract amplitude of low-frequency fluctuation (ALFF) features, leveraging its expanded feature channels to effectively mine regional neurodynamic properties. Furthermore, to overcome the representational bottlenecks of classical linear operators in parsing hidden, long-range network connections, we introduce quantum computing, exploiting its exponentially expansive state space and intrinsic low-parameter regularization mechanism. Guided by these properties, the quantum branch utilizes a variational quantum graph convolutional (QGCN) module—featuring a trainable circular encoding strategy and a hardware-efficient 4-qubit configuration—with a 2-layer nested message passing structure to process the functional connectivity (FC) matrix, harnessing quantum interference in Hilbert space to parse complex topology while effectively mitigating overfitting on small-sample medical data. A unified training scheme achieves full-dimensional fusion of node activity and topology. Achieving 68.49% accuracy, our method outperforms 10 classic and recent new baselines, providing a powerful computational intelligence tool for sensor-based ASD clinical diagnosis. Furthermore, interpretability analysis successfully maps core disease hubs to standard AAL116 atlas coordinates, providing a powerful tool for computationally aided ASD diagnosis. Full article

Accurate and rapid detection of Volvariella volvacea (straw mushroom) fruiting bodies at harvestable maturity is a critical prerequisite for automated industrial cultivation. However, existing detection methods often yield high false-negative and false-positive rates when processing a small-scale, densely distributed, and heavily occluded targets against complex straw substrate backgrounds. Furthermore, these methods frequently struggle to balance the competing requirements of architectural efficiency (such as parameter volume and computational complexity) and real-time performance for edge computing. To address these challenges, this study proposes a YOLO11n-CPDM, a lightweight detection model based on an improved YOLO11n architecture. The model incorporates synergistic optimizations across feature extraction, fusion, and reconstruction. First, a Dual Coordinate Attention Feature Extraction mechanism is integrated into the C3k2 bottleneck blocks of the backbone network. This enhances target perception in complex, occluded environments by concurrently modeling global context and local salient features. Second, within the neck network, the standard attention module is replaced with the PnPNystraAttention module, coupled with the DySample dynamic upsampling operator. This modification strengthens contextual relationships among multi-scale features and improves spatial consistency during reconstruction while preserving linear computational complexity. Finally, the detection head is optimized using MBConv blocks based on an inverted residual structure to minimize parameter volume. Experimental results on a custom V. volvacea dataset demonstrate that the proposed YOLO11n-CPDM model achieves significant performance gains, with Precision (P), Recall (R), and Mean Average Precision (mAP50) reaching 86.8%, 87.5%, and 88.4%, respectively. These figures represent improvements of 2.7, 3.0, and 3.2 percentage points over the baseline YOLO11n model. Additionally, the model size is reduced to 4.8 MB (a 12.7% decrease), while achieving inference speeds of 42.7 FPS on Jetson AGX Orin and 21.2 FPS on Jetson Nano, outperforming the baseline model on both embedded platforms. Consequently, the proposed model effectively enhances detection performance in complex environments while maintaining excellent lightweight characteristics and deployment flexibility, providing a solid technical foundation for intelligent perception and automated harvesting of V. volvacea. Full article

Maintaining reliable communication while ensuring collision-free motion is a central challenge for mobile relay robots operating in indoor disaster environments, where abrupt non-line-of-sight (NLOS) degradation and narrow structural bottlenecks can severely disrupt multi-hop connectivity. To address this problem, this paper proposes a Gaussian Process-Driven Adaptive Tube Model Predictive Control (GP-ATMPC) framework for communication-preserving relay navigation. Gaussian process regression (GPR) is used to construct a probabilistic spatial radio map from sparse received signal strength indicator (RSSI) measurements, providing both the predicted channel mean and its uncertainty over unvisited regions. Motion uncertainty is represented by an adaptive ellipsoidal error tube whose radius varies with translational motion, angular motion, and localization uncertainty. Based on this tube model, both obstacle and communication constraints are tightened over the full closed-loop state tube via a tube-tightened lower confidence bound (LCB) that jointly accounts for radio-prediction and motion-tracking uncertainty. Across two indoor disaster environments and 50 Monte Carlo runs each, the proposed method attains the highest connectivity satisfaction rate among controllers that preserve a safe motion margin, with significantly fewer end-to-end connectivity violations than nominal and heuristic adaptive-margin MPC by a paired Wilcoxon test, while maintaining millisecond-level online solve times. A reactive connectivity-first baseline reaches slightly higher raw connectivity but at three to four times the near-collision rate and without feasibility or stability guarantees. The radio-prediction layer is further validated in a higher-fidelity Gazebo environment and on real indoor RSSI measurements, where it reconstructs the measured channel with a mean absolute error of about 2.1 dB. These results indicate that coupling spatial radio prediction with adaptive tube-based robust control provides an effective framework for resilient communication-aware relay navigation in degraded indoor environments. Full article

Introduction: Biological invasions constitute one of the main threats to freshwater ecosystems, causing significant ecological changes through the introduction of exotic species that compete with or prey upon native species. In Portugal, the introduction and spread of exotic species in lotic and lentic ecosystems, such as pike (Esox lucius), European catfish (Silurus glanis), and largemouth bass (Micropterus salmoides), all top predators, may have a big impact on autochthonous species. In contrast, bleak (Alburnus alburnus), European perch (Perca fluviatilis), and common carp (Cyprinus carpio) compete aggressively for food resources. In the Sousa River basin, gudgeon (Gobio lozanoi) is considered an exotic species with potential ecological impact, with the minnow (Phoxinus phoxinus) stand having been recently identified in Portugal and, so far, recorded only in this river basin, and not yet being classified as an invasive species in Portugal. Public knowledge regarding invasive aquatic biodiversity remains a significant bottleneck for conservation. Because recreational angling is a prominent dispersal vector, initiatives that directly target this community are relevant. Objective: The aim is to carry out a bibliographic review on the exotic ichthyofauna species present in the Sousa River, with special focus on the invasion potential of the minnow (P. phoxinus). Methodology: The literature review was conducted based on the ScienceDirect, Springer Nature Link, and Fauna Norvegica databases, selecting publications between 2006 and 2025 concerning relevant studies on the potentially invasive characteristics of the minnow (P. phoxinus). The methodology is based on the analysis of studies regarding the impacts caused on riparian ecosystems. Results: The species P. phoxinus presents a generalist diet and high adaptive capacity, allowing it to colonise new habitats and compete aggressively with native species for trophic resources. Its presence is associated with negative impacts on brown trout populations (Salmo trutta), reducing growth and productivity, especially in mountain ecosystems. Increased species density also causes a significant decrease in benthic macroinvertebrate biodiversity. Studies conducted in the Douro basin indicate that the arrival of minnow in Portugal resulted from human action, probably associated with its use as live bait in recreational fishing. Conservation programmes use diverse tactics to bridge the awareness gap. Recent initiatives feature electrofishing demonstrations to visually differentiate species, theatrical performances, and even culinary show-cooking events using invasive predators like the European catfish to promote harvesting. Conclusions: The potential transition of P. phoxinus into an exotic and invasive species may be associated with the ecological pressure exerted on native communities, particularly through competition for trophic resources, highlighting the need to assess its dispersion in the Sousa basin and its impacts on fish fauna and benthic macroinvertebrates. It is important to do more sampling to understand its real distribution in the Sousa Basin. Additionally is important to explain to recreational anglers and the general population the impacts of fish transfer and the adverse effects of invasive species on freshwater Portuguese ecosystems. Full article

With the rapid development of the hydrogen economy, Type IV composite pressure vessels have emerged as the core components of on-board hydrogen storage systems. However, accurate fatigue life prediction remains a critical bottleneck limiting their design optimization and safe operation. Existing methods often exhibit prediction errors exceeding ±50% due to the inherent scatter, anisotropy, and complex service environments of composites. This study proposes an improved simulation method for fatigue life prediction of Type IV cylinders. Systematic tension–tension fatigue tests were conducted on carbon fiber-reinforced polymer (CFRP) laminates at four ply angles (0°, ±15°, ±30°, ±45°) and PA6 liner at three temperatures (−30 °C, 25 °C, 82 °C) to establish comprehensive S-N curve databases. The results reveal that ply angle is the predominant factor governing CFRP fatigue performance, while temperature significantly influences PA6 behavior, and failure mode transitions from fiber fracture to matrix-dominated damage as ply angle increases. A fatigue analysis model was developed in nCode, incorporating the ply fatigue Algorithm to characterize the anisotropic fatigue behavior of CFRP overwraps. Full-scale validation on Type IV cylinders under cyclic pressure (2–87.5 MPa) confirmed the method’s effectiveness, achieving prediction errors of 11.5% and 35.3% for the two failed specimens, with failure locations well predicted. This study provides a rapid and reliable engineering calculation method and data support for the anti-fatigue design, safety assessment, and life management of Type IV cylinders. Full article