Search Results (19,714)

Herein, an electrochemical sensor is reported for the first time based on an ordered macro–microporous composite derived from metal–organic frameworks (MOFs) for the highly sensitive detection of auramine O (AO), a Group 2B carcinogen. The hierarchical pore architecture, integrating an ordered macroporous network with a microporous ZIF-8 framework, enables the uniform dispersion of a high density of catalytically active sites. The interconnected macroporous channels facilitate efficient mass transport and rapid removal of reaction byproducts, effectively preventing pore blockage and ensuring stable sensing performance during repeated measurements. Owing to these structural advantages, the proposed sensor exhibits outstanding analytical performance toward AO detection, with a sensitivity of 0.4843 μA μM⁻¹, a detection limit of 0.168 μM (S/N = 3), and a wide linear range from 0.5 to 50 μM. Moreover, the sensor demonstrates excellent selectivity and reproducibility, maintaining reliable responses even in the presence of 100-fold excess common food constituents such as tartrazine and glucose. Real sample analysis further confirms its high accuracy and operational stability. Overall, the electrochemical sensor based on silver nanoparticle-decorated ordered macro–microporous ZIF-8 synthesized via in situ reduction shows great potential as a portable and on-site tool for rapid AO detection in food. More broadly, ordered macro–microporous MOF-derived materials represent a promising platform for advanced electrochemical sensor applications. Full article

The Korteweg–de Vries (KdV) equation is a foundational model in geophysical fluid dynamics (GFD), governing the propagation of long internal and surface gravity waves in stratified and shallow ocean environments where the interplay between nonlinear steepening and frequency-dependent dispersion gives rise to solitons. Although the analytical tractability of the KdV equation through inverse scattering is well established, systematic numerical exploration of multi-soliton interactions remains valuable for benchmarking solvers, probing conservation properties under varied oceanic initial conditions, and building intuition for more complex ocean wave phenomena. This article presents sangkuriang, an open-source Python library that solves the KdV equation using Fourier pseudo-spectral spatial discretization and adaptive eighth-order Runge–Kutta time integration. The implementation leverages just-in-time (JIT) compilation to achieve research-grade computational efficiency on standard hardware, making it readily accessible for coastal and ocean engineering applications, including idealized modeling of internal solitary waves on continental shelves, rapid parameter studies for solitary wave propagation in stratified basins, and pedagogical investigations of nonlinear dispersive wave dynamics. The solver is validated through four progressively complex idealized scenarios motivated by oceanic wave dynamics: isolated soliton propagation, symmetric interactions, overtaking collisions, and three-body interactions. High-fidelity conservation of mass, momentum, and energy is demonstrated, with relative errors remaining below $\mathcal{O}\left({10}^{-4}\right)$ across all test cases. Measured soliton velocities align with theoretical predictions within 5%, confirming the capture of the amplitude-dependent dispersion characteristic of oceanic solitary waves. Complementary diagnostics, including spectral entropy and recurrence quantification analysis (RQA), verify that the numerical solutions preserve the regular phase-space structure characteristic of integrable Hamiltonian systems. These results establish sangkuriang as a robust, lightweight platform for reproducible numerical investigation of idealized nonlinear dispersive wave dynamics relevant to coastal and ocean engineering applications. Full article

Declining birth rates are reshaping higher education across East Asia, accelerating the large-scale underutilization and, in some contexts, partial abandonment of university campus assets. Although adaptive reuse has been widely discussed, campus transformation is often framed primarily as a programmatic or policy problem, with limited attention to the inherited spatial logic embedded in campus morphology. This study revisits Le Corbusier’s concept of unlimited growth as a generative framework for campus transformation. Rather than treating it as a museum-specific historical typology, the research reinterprets unlimited growth as a scalable spatial logic defined by modular continuity, circulation hierarchy, and open-ended sequencing. To enhance reproducibility and operational clarity, the study formalizes a typo-morphological decoding protocol—modules, circulation, and growth sequence—and applies it through plan-, section-, and diagram-based analysis. Through comparative examination of three museum precedents—Sanskar Kendra Museum, the National Museum of Western Art (Tokyo), and the Chandigarh Museum and Art Gallery—the study extracts a set of transferable spatial mechanisms: modular increment, circulation-centered ordering, directional displacement, and fifth-façade ecological continuity. These mechanisms are then translated into an operational right-sizing model and tested through a design-operational demonstrator on a single anonymized Taiwanese campus experiencing demographic contraction. The findings indicate that unlimited growth functions not merely as a formal principle but as a spatial governance logic that supports phased consolidation, adaptive recomposition, and system-level coherence under long-term uncertainty. Importantly, this framework contributes to sustainability by reducing land consumption through spatial consolidation, minimizing unnecessary new construction, enabling adaptive reuse of existing campus assets, and improving long-term resource-use efficiency through phased right-sizing and ecological continuity. This study further advances a reproducible, mechanism-based methodological framework for institutional spatial transformation, providing a transferable approach for large-scale campus restructuring under conditions of long-term demographic and environmental uncertainty. Full article

It is difficult to capture the realities of the illicit antiquities market because of the lack of accessible, unsiloed data from underground trade networks. Despite existing literature on social network analyses and machine-learning experiments with antiquities data, there is a gap in simple open-source methodologies accessible to the non-academic public. By using a provenance-based analysis, we present a case study of the Italian antiquities trafficking networks that more fully captures their complexity. This study culls provenance data from repatriated antiquities gathered in the Museum of Looted Antiquities’ dataset to create a network visualization for analysis. Using open-source provenance and repatriation data from 1950 to July 2025, we built a dataset of 233 repatriation events with 15.858 objects to produce a network that reveals central actors, roles, and locations while staying within ethical privacy limits. This study captures large portions of the trafficking network by using accessible data and produces a reproducible, ethically framed workflow. Full article

Extraordinary emergencies trigger disruptive demand surges that frequently exceed the operational limits of existing supply chains. While traditional studies focus on optimizing stock resource efficiency, the mobilization of emergency production to generate incremental resources is critical under extreme shocks. However, a standardized methodology for assessing the “stress tolerance limit” of emergency material supply chains (EMSCs) remains lacking. This paper establishes a theoretical framework for EMSC stress testing, integrating conceptual definitions, operational mechanisms, and a standardized implementation procedure. To demonstrate its practical applicability, a multi-objective mathematical model is developed and applied to a case study of ibuprofen production during a sudden crisis. By identifying structural bottlenecks such as production latency and supply lead-time gaps, the results validate that the proposed framework provides a reproducible quantitative approach for evaluating EMSC supply capacity. This study offers guidance for prepositioned inventory and dynamic capacity reserve, fundamentally enhancing societal risk mitigation capabilities under extreme stress. Full article

Nanoemulsions are widely recognized as versatile delivery platforms capable of stably loading lipophilic active ingredients. Although low-energy phase inversion methods enable nanoemulsion formation under ambient and low-shear conditions, their scalability and applicability in practical formulation environments remain insufficiently validated. Here, we develop oil-in-water (O/W) nanoemulsions via a low-energy phase inversion process and systematically investigate their composition-dependent formation, scalability, and formulation stability. By precisely tuning the composition of a mixed nonionic surfactant system, monodisperse nanoemulsions with an average droplet size of ~110 nm and a polydispersity index (PDI ≤ 0.20) are reproducibly obtained under ambient, low-shear conditions. The optimized nanoemulsions maintain their nanoscale dispersion characteristics over 30 days of storage and exhibit consistent droplet size and distribution upon scale-up to 1 L. Furthermore, the nanoemulsions retain structural stability when incorporated into polymer-based formulations under various temperature conditions and repeated thermal cycling. These results demonstrate that low-energy phase inversion enables a scalable and formulation-compatible nanoemulsion platform, providing practical guidelines for industrial formulation and manufacturing of delivery systems for lipophilic active ingredients. Full article

Background/Objectives: Traditional histopathologic grading of renal cell carcinoma (RCC) is subjective, is poorly reproducible, and fails to predict responses to modern targeted agents or immunotherapies. In the era of precision oncology, molecular pathology offers objective tools for individualized management. We aimed to characterize genomic alterations in clear-cell RCC (ccRCC) with venous tumor thrombus and to develop pathology-driven panels for personalized prognostic stratification, with exploratory assessment of their potential to predict therapeutic response. Methods: Formalin-fixed paraffin-embedded pT1 ccRCC samples with and without thrombus underwent whole-exome sequencing. Distinct somatic mutations and copy number variations were incorporated into multigene panels. External assessment was performed in TCGA and PAWG cohorts, assessing survival outcomes and therapeutic biomarkers including homologous recombination deficiency (HRD), tumor mutational burden (TMB), and microsatellite instability (MSI). Results: Thrombus cases showed unique genomic heterogeneity compared with matched controls. Three multigene panels were constructed. Across external datasets, including a 354-patient TCGA-KIRC ccRCC cohort, the panels provided consistent molecular stratification signals for overall, disease-specific, and progression-free survival, complementing established pathological risk factors. They were significantly associated with established therapy-related genomic biomarkers, including HRD, TMB, and MSI, showing high sensitivity and negative predictive value in identifying patients unlikely to harbor these biomarker-positive profiles. These findings support the panels’ prognostic utility, with exploratory evidence for their potential in therapy response prediction. Conclusions: Small ccRCC with thrombus harbors distinct molecular pathological features. The proposed pathology-driven panels, compatible with FFPE tissue, represent pathology-compatible genomic tools that may support modern precision pathology by improving molecular risk stratification and informing exploratory therapeutic biomarker assessment. Full article

Melissopalynological analysis is essential for determining the botanical origin of honey, corbicular pollen and bee bread, as well as detecting adulteration. However, it traditionally relies on labor-intensive and subjective manual pollen identification. As a proof-of-concept preceding full honey analysis, this study evaluates artificial intelligence methods for automated pollen grain recognition under controlled conditions. Hazel (Corylus avellana L.) and dandelion (Taraxacum officinale F.H. Wigg.) were used as model taxa to validate the proposed approach before its application to real varietal honey samples. This study introduces a novel three-stage pipeline that decouples object detection from feature extraction, utilizing YOLOv12m for region-of-interest generation and, for the first time in melissopalynology, DINOv3 ConvNeXt-B for deep feature representation. Microscopic images acquired at 400× magnification yielded 2498 dandelion and 1941 hazel pollen grains. The detector achieved an mAP@0.5 of 0.936 with an F1 score of 0.88, while the classifier reached 98.1% accuracy with good class separability (Silhouette coefficient: 0.407). The primary technical contribution is the systematic optimization of the detection-to-classification interface. Context-aware bounding box expansion (12%) and an optimized IoU-NMS threshold (0.65) significantly improve the stability of morphological feature extraction, as confirmed by ablation studies. Computational cost reporting further supports reproducible, deployment-oriented comparison. The results confirm the feasibility of this AI-based framework as an intermediate step toward automated melissopalynological analysis, with future work focusing on standardized microscopy protocols and expanded pollen databases for varietal honey authentication. Full article

The rapid integration of generative artificial intelligence into scientific publishing is reshaping how academic text can be produced, revised, and scaled. While transparent and limited use of AI for language support may be acceptable, a new structural vulnerability may be emerging in medical publishing: the large-scale production of short, plausible, and weakly individualized correspondence across multiple specialties. In this viewpoint, we describe and conceptualize a pattern that may be termed synthetic authorship, defined not as undisclosed AI use alone, but as a reproducible mode of scholarly output structurally facilitated by automation. We focus particularly on letters to the editor, a format that combines brevity, rapid editorial handling, and formal indexation, and may therefore be especially exposed to this phenomenon. Based on recurring patterns observed in PubMed-indexed literature, including unusually high publication velocity, abrupt thematic dispersion, and stylistic uniformity across unrelated domains, we argue that such outputs may challenge the authenticity, epistemic value, and editorial function of scientific correspondence. We do not present empirical proof of misconduct, but rather outline a conceptual framework for understanding this emerging risk and propose proportionate editorial safeguards, including cross-domain pattern detection and contextual assessment of authorship coherence. As AI lowers the threshold for generating domain-plausible commentary at scale, scientific publishing must adapt its integrity frameworks accordingly. In this context, vigilance toward synthetic authorship may become an essential component of editorial responsibility and post-publication quality control. Full article

Dragonflies exhibit remarkable flight stability in unsteady environments, largely due to aerodynamic interaction between their forewings and hindwings. This study investigates gust response in dragonfly-inspired micro-aerial vehicles (MAVs) from a system dynamics perspective, with emphasis on the aerodynamic role of tandem-wing interaction rather than control compensation. A six-degree-of-freedom (6DOF) rigid-body framework is developed and coupled with a quasi-steady aerodynamic model that includes explicit phase-dependent interaction between forewing and hindwing forces. Gusts are introduced as time-varying inflow perturbations, allowing physically consistent analysis of how disturbances propagate through aerodynamic loading into vehicle motion. Simulations are performed for representative flight conditions, including gliding, hovering, and gust-perturbed ascent. The results show bounded trajectory, velocity, and attitude responses under sustained gust excitation, even with conservative baseline control. Force and energy analyses indicate that wing–wake interaction redistributes aerodynamic loads in time and reduces peak force and moment fluctuations before they reach the rigid-body dynamics. This behavior is interpreted as passive aerodynamic filtering of gust disturbances inherent to the tandem-wing configuration. Comparative simulations using backstepping control and Active Disturbance Rejection Control (ADRC) further show that the dominant gust attenuation arises from aerodynamic configuration rather than from control action. Although the aerodynamic model is quasi-steady, the framework reproduces key trends reported in biological and CFD-based studies and provides a numerical foundation for future wind-tunnel and free-flight experiments on configuration-level gust attenuation. Full article

Magnetotelluric (MT) inversion models are widely used to interpret crustal conductivity structures related to fluid pathways, deformation zones, and mineralization processes; however, evaluating the reliability of inversion-derived anomalies remains challenging when datasets contain subtle station-dependent distortions or residual cultural noise. We present a jackknife-inspired, model-space diagnostic framework based on leave-one-station-out (LOSO) inversion to quantify station influence and improve interpretation reliability. The workflow consists of (1) generating a LOSO inversion ensemble using identical inversion settings, (2) computing ensemble statistics and standardized perturbation metrics to identify sensitive zones, and (3) applying distribution-based diagnostics to classify station influence and guide construction of an ensemble-refined model. Synthetic experiments demonstrate that the framework distinguishes localized station-controlled artifacts from broadly supported structural responses, allowing targeted correction without altering robust features. Application to a field MT dataset acquired in a noise-affected environment shows that a mid-crustal conductive structure remains stable across the LOSO ensemble, while some shallow anomalies exhibit strong station dependence. The resulting ensemble-refined model introduces only localized modifications, demonstrating that ensemble-based model-space diagnostics provide a practical and reproducible strategy for validating MT inversion results and improving confidence in exploration-oriented conductivity interpretations. Full article

Cardiovascular-kidney-metabolic (CKM) syndrome integrates excess adiposity, metabolic dysfunction, kidney impairment, subclinical cardiovascular diseases, and clinical events along a staged continuum that invites unified prevention and treatment. Ultra-processed foods (UPFs) are a complex, high-prevalence exposure that may influence risk across CKM stages through nutrient profiles, additives, processing-induced compounds, and packaging-related contaminants. This review synthesizes epidemiologic, mechanistic, and translational evidence with attention to exposure definition and analytic rigor. We summarize NOVA-based UPF operationalization across dietary assessment tools, highlighting misclassification of mixed dishes, brand heterogeneity, and energy under-reporting, and we propose further examination of energy-adjusted models, calibration, and harmonized metrics. Observational studies consistently associate higher UPF intake with adiposity, diabetes, chronic kidney disease, cardiovascular events, and mortality, with modest to moderate effect sizes that are heterogeneous across populations. Mechanistic data from metabolomics, lipidomics, proteomics, and the gut microbiome converge on pathways of inflammation, lipid metabolism, oxidative and metabolic stress, and intestinal barrier dysfunction; in selected cohorts, multi-omics modules account for a substantial minority of UPF-outcome associations. We outline quality-control pipelines, batch-effect prevention/correction, and multiple-testing control necessary for reproducible diet-omics. Translationally, targeted lipidomic and proteomic panels show promise for CKM risk stratification and monitoring but require validation, clinical thresholds, and guideline endorsement. Equity and global context, including differences in product mix, food systems, and care capacity, modify population impact. We conclude with a research agenda prioritizing harmonized exposure metrics, error-aware modeling, standardized multi-omics workflows, and adequately powered, stage-specific interventions capable of testing mediation and prognostic utility. Full article

To perform screening of the retina on a population scale, an automated procedure is required that incorporates accurate, reproducible, interpretable, and computationally costeffective models. Existing approaches using convolutional or transformer architectures typically do not adequately represent both fine-grained pathology and large-scale retinal context simultaneously, which could adversely affect their reliability if used for large-scale applications in clinical practice. In this paper, we propose a hierarchical transformer-based screening framework for retinal fundus images that incorporates patch-based tokenization, global transformer encoding, and hierarchical aggregation of contextual information. We also developed a lightweight prediction head that supports screening for both single and multiple diseases. The framework has been evaluated using standard screening metrics, robustness, and cross-dataset generalization analyses on two eye retinopathy image databases: EyePACS and RFMiD. With regard to screening for a binary outcome of diabetic retinopathy, our method provided an accuracy of 89.4% and an area under the receiver operating characteristic (AUROC) curve of 93.6% on EyePACS and attained an accuracy of 95.2% and a macro-averaged F1 score of 82.7% on RFMiD. Our hierarchical transformer achieved improved robustness to degraded images and increased generalizability across datasets compared with all current state-of-the-art models. The proposed hierarchical transformer demonstrates strong potential for large-scale retinal screening and provides a promising foundation for future clinically validated deployment. Full article

In recent years, pigeon rotavirus A (PiRVA) infection, an important emerging disease, has posed a major threat to the healthy development of the pigeon industry and public health. Therefore, developing an accurate, rapid and convenient detection method for this virus is vital for monitoring and early diagnosis of the disease. In this study, on the basis of the ORF sequence characteristics of the PiRVA VP6 gene, crRNA and reverse transcription recombinase-aided amplification (RT-RAA) primers were designed. On the basis of the CRISPR/Cas12a system, for the first time, the RT-RAA-CRISPR/Cas12a rapid detection method of PiRVA was established by combining RT-RAA and lateral flow strips. This method could specifically detect PiRVA, and there was no cross-reaction with other common viruses originating from pigeons. The minimum detection limit was 16.8 copies/μL, and the results of the intrabatch and interbatch repeated tests were consistent. Moreover, the method established in this study and the previously established common PCR method were used to analyse 56 clinical tissue samples from racing pigeons and domestic pigeons collected in 2025. The positive rates of racing pigeon and domestic pigeon samples detected by PCR were 17.6% and 12.8%, respectively, and the positive rates of racing pigeon and meat pigeon samples detected by the RT-RAA-CRISPR/Cas12a method were 23.5% and 17.9%, respectively, indicating that PiRVA infection occurs in both racing pigeon and domestic pigeon populations in China. In summary, the PiRVA RT-RAA-CRISPR/Cas12a detection method established in this study has good specificity, sensitivity, and reproducibility, and allows visualization of the results, which can be used for field applications. This study provides technical support for epidemiological surveillance and etiological research on PiRVA. Full article

This paper presents three hybrid AC/DC topologies for the CIGRE European low-voltage benchmark grid to evaluate their impact on voltage regulation, current compliance, and power-sharing capability under realistic operating conditions. The proposed topologies integrate a dedicated DC network in parallel with the existing AC infrastructure through voltage source converters (VSCs), enabling controlled power exchange between the two subsystems. This structure facilitates improved voltage support and more flexible integration of distributed renewable energy resources, many of which inherently operate in DC. A decentralized droop-based control strategy is employed as a uniform baseline to control the VSCs and assess the intrinsic performance of each topology. The proposed architectures are evaluated using realistic 24-h load profiles under scenarios with and without droop control. The results demonstrate significant improvements in voltage stability and feeder current management, particularly under high DC penetration conditions. Overall, the study provides a reproducible benchmark framework for topology-level comparison of hybrid AC/DC low-voltage distribution networks. Full article