Search Results (3,277)

Rapid, accurate, and scalable ion sensing technologies are highly desirable for future flexible healthcare and lab-on-a-chip applications. Here, we present a fully roll-to-roll (R2R) gravure-printed single-walled carbon nanotube complementary ring oscillator (SWCNT-cRO)-based microfluidic ion sensing platform fabricated on a flexible substrate. The proposed platform combines scalable printed complementary electronics with frequency-based ion sensing via electrostatically induced top-gating in aqueous microfluidic environments. The fabricated SWCNT-cRO devices exhibited stable oscillation characteristics, with a high device yield (>80%) and continuous manufacturing capability at a web speed of 5.4 m/min. Printable ethanolamine/zirconium acetylacetonate-based n-doping technology enabled complementary SWCNT transistor operation, while multilayer CYTOP/FG-3650 encapsulation ensured stable electrical operation under ionic aqueous conditions. After integration into a polydimethylsiloxane-based microfluidic channel, the oscillation frequency of the SWCNT-cRO was systematically modulated by Na⁺ concentration and pH. The sensing mechanism was based on electrostatically induced carrier modulation in n-type SWCNT transistors, resulting in variations in propagation delay and corresponding shifts in oscillation frequency. Compared with conventional ion-sensitive transistor platforms, the proposed approach offers scalable manufacturing, non-contact ion sensing, elimination of external reference electrodes, and direct compatibility with digital frequency-signal processing systems. This work establishes a promising strategy for future low-cost, disposable, and flexible microfluidic sensing platforms for wearable healthcare and lab-on-a-chip applications, ion sensing, and thin-film transistors. Full article

Fine-grained insect pest classification presents a particularly demanding visual recognition challenge due to severe class imbalance, pronounced intra-class morphological variability across developmental stages, and high inter-class visual similarity among taxonomically related species. Existing deep learning approaches typically rely on a single feature representation extracted from a single network depth, overlooking complementary discriminative cues distributed across multiple abstraction levels. Furthermore, classical attention mechanisms perform spatial weighting deterministically, without explicitly modeling the underlying statistical structure of the feature space, which is inherently multimodal on long-tailed benchmarks such as IP102. This study proposes a Multi-Scale Gaussian Mixture Model-Gated Mixture of Experts (GMM-MoE) architecture that operates as a plug-in module insertable into any convolutional backbone, evaluated here on DenseNet-121 at three distinct feature depths. The proposed module computes analytic GMM posterior responsibilities in closed form, softly assigning each spatial location to dedicated convolutional expert sub-networks. At the same time, a conditional prior mechanism π(x) adapts the routing strategy to individual image content rather than relying on fixed priors. The architecture is evaluated on the IP102 benchmark (102 pest classes, ~75,000 images) under a two-stage training protocol. Ablation experiments confirm that increasing the number of experts consistently improves accuracy across all three routing depths, and that multi-scale fusion surpasses any single-scale configuration. The proposed model achieves a mean top-1 accuracy of 74.12% (±0.25%, 95% CI) across three independent runs on the IP102 test set. To the best of our knowledge, this is the first work to employ GMM posterior responsibilities as a spatial routing mechanism within a multi-scale CNN feature hierarchy for fine-grained insect pest classification, establishing a principled probabilistic alternative to deterministic attention weighting in visual recognition systems. Full article

Against the backdrop of environmental governance systems transitioning from command-and-control to multi-stakeholder collaboration, elucidating the mechanisms and pathways through which voluntary environmental regulations influence green technological innovation in heavily polluting enterprises holds significant implications for advancing green innovation and high-quality development. This paper systematically examines the synergistic mechanisms of command-and-control versus voluntary environmental regulations on green technological innovation in heavily polluting enterprises, utilising data from listed companies in China’s high-pollution industries between 2008 and 2024. Unlike previous studies predominantly focused on the impact of a single regulatory type, this study reveals an interactive effect between the two: moderate command-and-control regulation provides essential institutional support for voluntary environmental regulation, such as ISO 14001 certification, thereby generating a complementary enhancement effect. However, overly stringent command-and-control regulation diverts innovation resources from enterprises, thereby suppressing the incentive effect of voluntary regulation. This conclusion transcends the traditional analytical paradigm within environmental regulation theory that treats command-and-control and voluntary regulations as mutually exclusive opposites, revealing instead a dynamic relationship where both synergistic and constraining effects coexist. This discovery provides crucial theoretical underpinnings and empirical evidence for constructing an environmental governance system that combines command-and-control constraints with flexible incentives, ensuring compatibility between policy objectives and corporate behaviour. Full article

Visual explainable artificial intelligence (XAI) is an important mechanism for connecting analytically complex decision models with practitioners who must interpret and act upon their outputs in industrial supply chains. In facility localization problems, wafer foundries and other capital-intensive manufacturers must evaluate geographically dispersed candidate sites against multiple uncertain criteria. The ability to communicate fuzzy group decision-making (FGDM) outcomes in a transparent, interpretable form has direct operational relevance. The literature has introduced hanging gradient bar charts, gradient bidirectional scatterplots, and traceable aggregation charts as visual XAI instruments for semiconductor supply chain localization that show substantial reductions in interpretation error versus conventional plots. However, the quantitative assessment of explanation quality itself remains underdeveloped. To address such a gap, this research proposes a quantitative explainability quality index (XQI) that formalizes visual explanation quality in FGDM as a composite measurable construct. XQI integrates two complementary layers: (1) An objective explainability layer (OEI), consisting of normalized fuzzy interpretation deviation, response time, ranking fidelity, and interpretation accuracy, and (2) a subjective explainability layer (SEI), consisting of perceived understanding, perceived transparency, decision confidence, and cognitive load. Trust, acceptance, and decision quality are downstream outcome constructs rather than components of the index. A weighted linear combination of OEI and SEI produces a single index for systematic, reproducible comparison across competing visualization designs. A structural equation model is specified as a planned validation mechanism for examining how explanation quality may relate to trust, acceptance, and downstream decision quality. The proposed validation framework includes a semiconductor facility localization scenario, three visualization conditions, and a planned participant pool of 150–240 supply chain managers, engineers, and graduate students. The XQI framework transforms visual XAI from a descriptive communication aid into a testable decision-support construct, thereby addressing a key evaluation gap in the FGDM visualization literature. Full article

Background: Cardiac magnetic resonance (MRI) images often exhibit low contrast, anatomical variability, and indistinct boundaries, particularly in the myocardium (MYO) and right ventricle (RV). These challenges can reduce the reliability of both manual and automated segmentation, highlighting the need for more robust and boundary-aware approaches. Methods: In this study, an Aspect-Aware Boundary-Resilient UNet3D (ABR-UNet3D) architecture is proposed for cardiac MRI segmentation. The model incorporates an Aspect-Aware Complementary Attention (AAC) module that combines multi-planar contextual information with a complementary gating mechanism to enhance boundary representation. The method was evaluated on the ACDC dataset under consistent training conditions. In addition to Dice Similarity Coefficient (DSC) and Intersection over Union (IoU), boundary-based metrics, including the 95th percentile Hausdorff Distance (HD95), Average Surface Distance (ASD), and Surface Dice, were employed. Furthermore, a five-fold cross-validation protocol and detailed ablation studies were conducted to assess robustness and analyze the contribution of individual AAC components. Results: The proposed method achieved a mean DSC of 0.9603 in single-run experiments on the ACDC dataset and showed consistent performance in anatomically challenging regions, particularly for RV and MYO segmentation. In addition, five-fold cross-validation experiments resulted in an average DSC of 0.952 ± 0.009 and IoU of 0.908 ± 0.012, indicating stable performance across different data splits within the evaluated dataset. Boundary-based metrics also showed improved surface agreement and lower boundary errors compared with the evaluated baseline models. Ablation studies further indicated that the combined use of multi-planar contextual information and complementary gating contributes more effectively to segmentation performance than the individual components used separately. Conclusions: The results suggest that the proposed ABR-UNet3D architecture provides a stable and competitive segmentation framework for cardiac MRI images within the scope of the ACDC dataset. By jointly modeling contextual information and boundary refinement, the method improves segmentation reliability in challenging regions while maintaining competitive and consistent performance with respect to existing approaches. Full article

Background/Objectives: Marine-derived bioactive compounds have attracted increasing interest due to their potential antiviral properties. This study investigated in vitro antiviral activity of oil extracted from the green-lipped mussel (Perna canaliculus, GLM) and low-molecular-weight (LMW) fucoidan from Undaria pinnatifida against three human viruses in mammalian cell systems. herpes simplex virus-1 (HSV-1), respiratory syncytial virus (RSV), and SARS-CoV-2. These marine compounds were selected with the longer-term aim of evaluating their combination as a potential synergistic antiviral strategy. Methods: Antiviral efficacy was assessed using complementary assay platforms, including plaque reduction assays in mammalian cell systems and a lentiviral pseudovirus system delivering a bioluminescent reporter gene in HEK293/ACE2 cells pseudotyped with the SARS-CoV-2 spike glycoprotein. Cytotoxicity was assessed in parallel, and the selectivity index (SI) was calculated as the ratio of CC₅₀ to IC₅₀ for each compound and virus tested. Results: GLM oil showed potential antiviral activity against SARS-CoV-2 pseudovirus (SI > 6.20), with limited activity against RSV (SI > 3.48) and HSV-1 (SI > 2.28). In contrast, LMW fucoidan did not demonstrate antiviral activity against any of the tested viruses. Conclusions: These findings support further investigation of GLM-derived bioactive compounds as potential antiviral agents, including studies to elucidate their mechanisms of action and in vivo studies to confirm their antiviral efficacy. Combination studies were not pursued in the present work as both compounds require further optimisation individually; however, future studies should evaluate their combined antiviral potential, as synergistic or additive effects remain plausible. Full article

Essential oils have attracted increasing attention due to their bioactive properties. This review focuses on their anticonvulsant potential by exploring the relation between the chemical composition of essential oils and the mechanism of action underlying this effect. Evidence from in vivo and ex vivo studies is presented to identify structure–activity relations and to distinguish well-supported effects from preliminary findings. Moreover, as essential oil’s quality is vital to ensure safety and efficacy in pharmacotherapeutic approaches. For this reason, factors including extraction and analytical methods as well as authenticity assessment are discussed due to their impact on pharmacological consistency and reproducibility. Overall, this review highlights key compounds and mechanisms contributing to anticonvulsant activity, identifies current limitations in the literature, and outlines priorities for future research aimed at validating essential oils as potential complementary therapeutic agents in seizure management. Full article

Background: Metabolic dysfunction-associated steatotic liver disease (MASLD) represents a globally prevalent hepatic disorder, characterized by hepatic lipid accumulation and extrahepatic complications, notably intestinal barrier injury, which further exacerbates MASLD progression. The “gut–liver axis” has been identified as a critical contributor to MASLD development, with insulin-like growth factor 1 (IGF-1) serving as a pivotal coupling factor of this axis. However, the specific role and molecular mechanism by which IGF-1 modulates intestinal barrier function in the context of MASLD remains unclear. Methods: This study analyzed the correlations between the GH/IGF-1 axis and intestinal barrier function in MASLD rats, and explored the effects of IGF-1 intervention both in vivo and in vitro. Results: Our results showed that MASLD rats exhibited intestinal barrier impairment, characterized by elevated serum Diamine oxidase (DAO) and D-Lactate (D-LAC) levels, villus damage, and downregulation of tight junction proteins and Mucin (MUC2). These changes were accompanied by suppression of the GH/IGF-1 axis. Correlation analysis uncovered a negative association between IGF-1 levels and markers of barrier dysfunction. IGF-1 intervention effectively repaired the intestinal barrier structure of MASLD rats and significantly upregulated the expressions of IGF-1R, PI3K, and AKT. In vitro, IGF-1 treatment improved transepithelial electrical resistance (TEER), enhanced barrier-related gene expression, promoted cell proliferation, and inhibited apoptosis. Conclusions: These findings suggested that GH/IGF-1 axis suppression, intestinal barrier dysfunction, and IGF-1R/PI3K/AKT signaling were interconnected within the gut–liver axis in MASLD. IGF-1 may contribute to barrier regulation through associated signaling changes, highlighting the GH/IGF-1 axis as a potential complementary target. Full article

Automatic detection of sensitive fields in structured data is a critical prerequisite for privacy compliance and data governance. However, existing approaches face severe cross-domain generalization challenges. Hand-crafted pattern rules often fail under highly heterogeneous naming conventions, while single statistical models tend to overfit and degrade sharply under distribution shifts between training and deployment domains. These limitations stem from the weak semantic signals and distributional heterogeneity of structured data, which make it difficult to simultaneously capture explicit rules and latent, variant-sensitive attributes. To address these challenges, we propose a detection framework based on multi-view complementary features and a Hierarchical Gated Residual Network (HGRN). The framework first constructs a full-spectrum feature system that integrates explicit rules and implicit statistical fingerprints (e.g., entropy and character texture) to fill the semantic gap. It then introduces a decision mechanism combining robust priors with dynamic residual calibration: a random forest provides a stable probabilistic anchor, which is further nonlinearly corrected by a learnable gating-and-expert network. This design explicitly resolves the cognitive conflict between rule-dominated regions and complex distributional regions. Experiments on multiple real-world datasets—including DeSSI, CMS Open Payments and Home Credit—show that the proposed method achieves a Macro-F1 of 0.9408 on DeSSI and exhibits strong in-domain performance. Under strict frozen-model cross-domain transfer, HGRN mitigates the catastrophic collapse observed in pure neural baselines and maintains moderate detection capability, offering interpretable trust allocation between rule-based priors and data-driven correction in both financial and healthcare scenarios. Full article

Background: Sleep disorders, particularly insomnia, represent a major public health concern, while currently available hypnotic drugs are often limited by adverse effects and poor long-term tolerability. Methods: In this study, we investigated the sleep-promoting effects of a mixture of Hypericum perforatum L. and Melissa officinalis L. extract (HME) and its underlying mechanisms in male ICR and C57BL/6 mice. In a pentobarbital-induced sleep model in mice, sleep onset latency and total sleep time were measured. Pharmacological studies using various antagonists and agonists were conducted to elucidate receptor-mediated mechanisms. Immunohistochemical and immunofluorescence analyses were performed to assess neuronal activity, and cortical mRNA expression was evaluated by quantitative analysis. HPLC analysis was used to identify the major constituents of HME, and their pharmacological profiles were functionally evaluated. Results: HME significantly reduced sleep onset latency and prolonged total sleep time. These hypnotic effects were shown to be mediated through adenosine and melatonin receptor signaling pathways. Immunohistochemical and immunofluorescence analyses showed that HME suppressed neuronal activity in wake-promoting cholinergic and orexinergic neurons of the basal forebrain and lateral hypothalamus, while enhancing activation of sleep-promoting GABAergic neurons in the ventrolateral preoptic nucleus. At the molecular level, HME increased cortical mRNA expression levels of adenosine A₁ receptor, adenosine A_2A receptor, melatonin receptor ₁, and melatonin receptor ₂. From the HPLC analysis, rosmarinic acid and hyperoside were identified as the major constituents of HME. Functional evaluation of these compounds revealed complementary pharmacological profiles, with hyperoside primarily acting through adenosine receptors and rosmarinic acid engaging both adenosine and melatonin receptor pathways. Conclusion: These findings suggest that HME enhances both sleep initiation and maintenance through adenosine and melatonin receptor signaling pathways, thereby supporting its potential as a multitarget therapeutic agent for improving sleep quality. Full article

Background: Accurate brain tumor segmentation from Magnetic Resonance Imaging (MRI) depends on the fusion of multiple complementary modalities. However, clinical practice often faces incomplete modality sets due to acquisition failures, patient contraindications, or protocol variations. Current methods either treat each modality feature extractor in isolation or depend on computationally expensive teacher networks for cross-modal knowledge transfer. Objective: This paper presents Hierarchical Adaptive Group Self-Support Learning with Boundary-Aware Calibration (HAGSS), a framework that overcomes three key limitations of existing group self-support methods: static group formation that ignores temporal prediction quality, uniform treatment of boundary and interior voxels, and distribution mismatch across heterogeneous modality logits. Methods: We propose a hierarchical adaptive group formation mechanism that reassigns group leader roles at each epoch based on voxel-level prediction confidence scores instead of fixed sensitivity priors. We also introduce a boundary-aware calibration module that applies spatially varied distillation weights with greater emphasis on tumor boundary regions. In addition, we design a cross-scale consistency regularization term that enforces agreement between multi-resolution predictions to stabilize the self-support target. Results: Experiments on BraTS2020, BraTS2018, and BraTS2021 datasets show that HAGSS achieves consistent improvements over state-of-the-art baselines. The average Dice gains across the whole tumor, tumor core, and enhancing tumor regions reach 1.30% on BraTS2020 and 1.61% on BraTS2021 compared to existing methods. All improvements are statistically significant ($p<0.05$). Conclusions: HAGSS operates exclusively during training, adds no parameters or inference cost, and can be applied as a plug-in module to any multi-encoder incomplete multi-modal segmentation architecture. Code is publicly available at GitHub. Full article

Accurate traffic flow forecasting is essential for the stable operation and efficient scheduling of intelligent transportation systems. The key lies in identifying the complex spatio-temporal dependencies within the road network structure. In the real world, traffic data are often noisy and incomplete due to sensor failures, communication interruptions, and other unexpected disturbances. To overcome these challenges, this paper proposes an adaptive spatio-temporal self-supervised traffic flow forecasting method based on contrastive learning (ASTSS-CL). At the graph level, structural perturbations are generated by combining node centrality with nonlinear probabilities, while a learnable temporal-periodic parameter matrix and an attention-based fusion mechanism are introduced to adaptively optimize adjacency relationships. At the temporal level, complementary augmentations are designed in both the time and frequency domains. Dynamic interpolation captures continuous traffic variations, while wavelet decomposition and node-adaptive frequency masking balance low-frequency trends and high-frequency details; random masking further improves robustness to missing observations and disturbances. In addition, spatial heterogeneity learning and contrastive consistency learning are jointly employed to enhance representation quality. Experiments on the PeMS04 and PeMS08 datasets show that ASTSS-CL achieves MAE, RMSE, and MAPE values of 17.95, 28.86, and 12.07% on PeMS04, and 13.78, 22.05, and 9.46% on PeMS08, respectively, outperforming the best-performing baseline. These results validate the effectiveness of the proposed method and demonstrate its potential to support traffic management and the operation of intelligent transportation systems. Full article

Cross-noise fault diagnosis remains challenging due to the mismatch between training and testing noise conditions, which degrades feature reliability and model generalization. To address this issue, this paper proposes a reliability-guided adaptive feature fusion framework (RGAF-Net). The method focuses on sample-wise adaptive feature fusion, where the enhanced wide first-layer convolutional neural network(WDCNN) backbone is employed to improve multi-scale feature extraction under noisy environments. In addition, a dual-path architecture is introduced to provide complementary representations, including globally robust structural representations and locally detail-sensitive structural responses. Furthermore, a lightweight reliability estimation module is designed to characterize the signal degradation tendency under noisy conditions of each input sample, based on which a sample-wise routing mechanism dynamically adjusts feature contributions during feature fusion. Experiments on two public bearing datasets (PU and JNU) under cross-noise settings demonstrate that the proposed method achieves improved performance compared with representative approaches, particularly under severe noise conditions. For example, on the JNU dataset at −10 dB, the proposed method improves the Macro-F1 score by over 19 percentage points compared with the baseline WDCNN. Ablation studies and visualization analyses further demonstrate the effectiveness and adaptive fusion behavior of the proposed framework. The results indicate that the proposed method provides an effective solution for robust fault diagnosis under noise mismatch scenarios. Full article

We propose a structural framework for interpreting quantum computational advantage in terms of admissible continuation of configurations. In this framework, a quantum computation is described not only as a sequence of gates acting on a state vector but also as the organization of admissible histories whose phase contributions combine coherently in a manner related to sum-over-histories and path-integral formulations of quantum mechanics. We identify three structural features that are relevant to quantum advantage: the multiplicity of admissible histories, the degree of phase coherence among them, and the non-factorizable structure of continuation constraints corresponding to entanglement-like global dependence. To make these features explicit, we introduce the notion of effective coherent multiplicity, which measures the coherently usable portion of an admissible-history space before probability normalization. We then formulate a structural speedup conjecture: substantial quantum advantage requires not merely a large number of possible histories but scalable coherent multiplicity supported by non-factorizable constraints whose instability remains bounded. We also introduce a coherent-fiber criterion, which identifies phase-alignable families of histories selected by compact computational relations as a structural source of coherent amplification. This formulation does not replace standard complexity-theoretic measures such as circuit size, query complexity, or BQP membership. Rather, it provides a complementary structural language for relating those measures to interference, entanglement, decoherence, and the organization of computational history space. The framework clarifies, at a structural level, why raw branching alone is insufficient for speedup, why unstructured search yields only a limited advantage, and why problems with compact global regularities, such as Simon’s problem and period finding, can support stronger coherent amplification. The paper also discusses how the proposed quantities relate to standard notions, including success amplitudes, entanglement measures, tensor-network simulability, and fault-tolerance constraints. In this way, admissible-history structure is presented as a diagnostic viewpoint for understanding both the power and limitations of quantum computation. Full article

Apitherapy is a complementary therapeutic approach based on the use of bee-derived products, particularly bee venom (BV), also known as apitoxin. Bee venom is a complex mixture of biologically active compounds, including peptides, enzymes, and biogenic amines, that exhibit diverse pharmacological activities. Major bioactive constituents such as melittin, apamin, adolapin, and phospholipase A2 have attracted increasing scientific interest due to their anti-inflammatory, antioxidant, antimicrobial, analgesic, and immunomodulatory properties. This review provides a comprehensive overview of the biological effects and therapeutic potential of bee venom in the management of chronic diseases, particularly diabetes, cancer, and neurological disorders. Evidence from experimental and clinical studies suggests that BV and its components can modulate multiple molecular pathways associated with oxidative stress, inflammation, apoptosis, and immune responses. These mechanisms contribute to potential benefits in glycemic control, tumor suppression, neuroprotection, and pain management. Additionally, bee venom has been investigated for its capacity to influence signaling pathways involved in cellular proliferation and survival, highlighting its potential as a complementary strategy in the treatment of complex diseases such as neurodegenerative disorders, including Parkinson’s and Alzheimer’s diseases. Despite these promising therapeutic effects, the clinical use of BV remains limited due to safety concerns, particularly the risk of allergic reactions, systemic toxicity, and anaphylaxis. Recent advances in drug delivery systems and nanotechnology may help improve the safety and efficacy of BV-based therapies by enabling targeted delivery and controlled dosing. Overall, bee venom represents a promising source of bioactive compounds with potential applications in translational and integrative medicine; however, further well-designed clinical trials and mechanistic studies are necessary to establish its safety, efficacy, and long-term therapeutic value. Full article