Search Results (2,454)

Fluorescence remains a foundational optical phenomenon underpinning applications in sensing, imaging, diagnostics, and catalysis. Among the strategies developed to modulate fluorescence, coupling fluorophores with plasmonic metals has emerged as a powerful route for both enhancement and quenching. The collective excitation and decay of surface plasmons can profoundly alter fluorophore excitation rates, radiative pathways, and emission efficiencies. This review provides a mechanistic and historical synthesis of metal–fluorophore interactions, unifying enhancement and quenching phenomena under the term Metal Manipulated Fluorescence (MMF). We summarize the fundamental principles of fluorescence and plasmon resonance, discuss theoretical and computational approaches for predicting metal–fluorophore coupling, and critically examine recent advances in plasmonic nanostructure synthesis that enable precise control over fluorophore behaviour. By integrating experimental observations with theoretical models, we highlight the opportunities and limitations of current MMF strategies and outline future directions in materials design, synthesis methodologies, and predictive modelling for next-generation optical and optoelectronic technologies. Full article

Boiling crises are complex stochastic processes that are influenced by the physical phenomena of heat transfer and evaporation, as well as the shape and roughness of the boiling surface. When calculating the critical heat fluxes corresponding to the point of the first boiling crisis, it is important to know the numerical density of the formed bubbles per unit surface and volume. Most models consider only non-interacting bubbles. This greatly reduces their predictive accuracy. An analysis of the video footage of bubble boiling near the point of the first boiling crisis allows us to conclude that this is a typical picture for a continuum off-lattice problem of percolation theory. The main purpose of the work is to consider the point of the first boiling crisis as the percolation threshold for a three-dimensional problem. This threshold describes the transition from finite size inclusions (single bubbles and small groups of weakly interacting bubbles) to a percolation structure in which there is a macroscopic irregular bubble, the size of which is comparable to the size of the entire system. This hypothesis allows us to make estimates for the concentration of bubbles at the boiling point and to obtain estimates for critical heat fluxes at this point. The fundamental difference between the proposed approach and previous attempts to apply percolation theory to the description of boiling crises is the consideration of a three-dimensional problem in liquid volume, rather than a two-dimensional problem onto a hot boiling surface. It is shown, for the first time, that the proportionality constant in the Kutateladze–Zuber equation coincides with the percolation threshold for a three-dimensional continuum percolation problem on overlapping ellipsoids. Full article

Due to the complexity inherent in river ice dynamics, the utilization of remote sensing imagery represents the most crucial and effective method currently available for monitoring changes in river ice. In the Inner Mongolia segment of the Yellow River during winter, two distinct types of ice surfaces are observed: juxtaposed ice and consolidated ice. Additionally, certain areas of open water remain unfrozen. Rapid identification and classification of extensive ice formations and open water zones along this lengthy river section constitute critical information for informed decision-making in ice prevention and management strategies within the Yellow River basin. This paper takes the formation and characteristic analysis of different types of ice in the Yellow River channels in Inner Mongolia as the starting point. It employs a support vector machine (SVM) as the classifier and introduces an optimized model for classifying river ice types using high-resolution Sentinel-2 optical imagery. The model utilizes multi-band spectral features, along with multi-spectral fusion indices such as the normalized difference snow index (NDSI) and the normalized difference frozen surface index (NDFSI), as feature vectors. This approach attains an overall accuracy of 94.91% in classifying different types of ice and can significantly contribute to river ice monitoring by offering robust theoretical support. In the winter of 2023–2024, the proportion of juxtaposed ice on the Yellow River section in Inner Mongolia changed from 45% to 55%, the proportion of consolidated ice changed from 30% to 40%, and the proportion of open water changed from 9% to 19%. This research investigates the characteristics of river ice formations and develops a classification methodology for river ice patterns utilizing high-resolution Sentinel-2 imagery in conjunction with a supervised classification algorithm. The findings of this study are intended to offer technical support for the expedited interpretation of ice conditions in the Yellow River, thereby serving as a scientific basis for precise monitoring and effective disaster prevention and management related to river ice phenomena. Full article

The modeling of pesticide phenomena aids researchers and policymakers in reaching complex agricultural decisions. A contemporary global concern among consumers revolves around the presence of pesticide residues in food, which possesses significant threats to human health and non-target organisms. Throughout all parts of the world, pesticide concentration in food crops is determined by laboratory analysis. In a limited number of areas, predictions of pesticide concentration are performed by analytical or numerical modeling. In this study, a thorough review of pesticide models for predicting their concentrations is critically performed. Among the 53 papers examined in this study, 31 of them are based on a theoretical concept, four amalgamate empirical data with a theoretical background, ten of them are based on only experimental results, and lastly, eight are discussion articles describing observations from various field studies. Overall, more than 69% of the papers focus on direct pesticide–plant interactions which affect our food chain, while indirect effects on environmental components, especially surface water, also receive attention. To the best of our knowledge, among the limited reviews, this is the first to attempt to accumulate all such modeling information in one place, critically analyze all papers to identify their limitations and scopes, and finally provide future research directions in this area. Full article

Monoclonal antibodies represent one of the fastest-growing sectors of the biopharmaceutical industry. Their high therapeutic efficacy and reduced incidence of adverse effects compared to conventional therapies have led to an increasing demand for these products. The costliest stages of monoclonal antibody production are the separation and purification processes, which underscores the need for continuous development and optimization of applied methodologies. Active pharmaceutical ingredients must exhibit high purity and preserved biological activity in order to meet stringent regulatory requirements. Macromolecules such as monoclonal antibodies possess complex conformational structures that significantly influence their stability. The application of multi-step chromatographic processes during purification from cell culture harvests may induce structural alterations, including protein unfolding and aggregation, ultimately resulting in decreased product quality and therapeutic effectiveness. Such structural changes may also increase immunogenicity risk and reduce product shelf life, posing additional challenges for downstream processing. In addition, chromatographic media create microenvironments that differ markedly from bulk solution (e.g., high local protein concentration, confined pore spaces and heterogeneous surface chemistry). These effects can promote either self-association driven by colloidal interactions or partial unfolding followed by irreversible aggregation, depending on the unit operation and operating window. Practical mitigation is therefore rarely achieved by a single lever; instead, it requires an integrated view of resin selection, buffer composition (pH, salt type and ionic strength, and stabilizing additives), residence time and temperature, as well as an analytics strategy that combines orthogonal aggregation assays with structural probes. This work discusses the phenomena of unfolding and aggregation of therapeutic proteins, with particular emphasis on monoclonal antibodies occurring during chromatographic purification. Furthermore, key analytical methods, characterization techniques, and mitigation strategies aimed at improving product quality and reducing manufacturing costs are reviewed. Full article

With the advent of ubiquitous healthcare and advancements in textile industry, non-invasive wearable biomedical solutions are becoming an increasingly attractive alternative to in-hospital monitoring, allowing for timely diagnostics and prediction of severe medical conditions. Non-contact biopotential monitoring is particularly promising because non-contact biopotential electrodes can be applied over clothing or embedded in the material without almost any preparation. However, due to the intricacies of capacitive coupling they rely on, the design of such electrodes and their interface with the body plays a key role in achieving measurement repeatability and their widespread utilization in clinical-grade diagnostics. Based on exhaustive investigation of several decades of the literature on non-contact and capacitive biopotential electrodes and electric potential sensors, this study is intended to serve as a state-of-the-art overview of their historical development and design challenges, a collecting point for important research theories and development milestones, a starting point for anyone seeking for a soft head start into this research area, and a remedy for occasional misnomers and conceptual errors identified in the existing papers. The ultimate goal of this comprehensive analysis is to demystify phenomena of non-contact biopotential monitoring and capacitive coupling, systematically reconciliate terminological inconsistencies, and enhance accessibility to the most important findings for future research. To accomplish this, fundamental concepts are thoroughly revisited—from fundamentals of electrochemistry and working principles of capacitors and operational amplifiers to system stability and frequency-domain analysis. With the use of various mathematical tools (Laplace transform, phasors and Fourier analysis, and time-domain differential calculus), discussions on non-contact and capacitive biopotential electrodes, collected from the 1960s onward, are for the first time compiled into a unified, abstracted, bottom-up analysis. The laid-out inspection provides analytical explanation for various aspects of measurement results available in the referenced literature, but also serves an educative purpose by devising a methodological framework that can be easily applied to other similar research fields. Firstly, the differences and similarities between wet, dry, surface-contact, non-contact, capacitive, insulated, on-body, and off-body biopotential electrodes are clarified. For this purpose, equivalent electrical models of various non-invasive biopotential electrodes are analyzed and compared. As a result, a proposal for a revised classification of biopotential electrodes is given. Secondly, instead of using the concept of a purely capacitive biopotential electrode, a test is proposed for assessing the predominant coupling mechanism achieved with an electrode over an insulating layer. Thirdly, a fundamental model of a buffer active non-contact biopotential electrode and its interface with the body is built and generalized, and the proposed test is applied for analyzing the influence of voltage attenuation and phase shifts on signal morphology. Lastly, guidelines for designing the described electrode–body interfaces are proposed, along with a discussion on practical aspects of their implementation. Full article

In this paper, we show that the sphere-in-contact model can predict long-range surface adsorption phenomena based on adsorbate-adsorbate repulsions and their geometric distance, assuming that their negative surface-induced charge is smeared on the surface of the adsorbate atoms. Additionally, it can be used to model collective surface diffusion mechanisms such as the domino-type surface diffusion of adsorbate rows on close-packed metal HCP and FCC surfaces. We have recently shown that the sphere-in-contact model can be used as an educational and research tool in various contexts, such as the visualization of carbon structures (e.g., graphene, carbon nanotubes, carbon nanocones, and graphite), heterogeneous catalysts, metal nanoparticles, and organic molecules. Here we present how it can be used to model the adsorbate structure of monoatomic elements on the hexagonal close-packed surface of HCP and FCC metals to study long-range ordering phenomena of monoatomic adsorbates on metals. We have used atoms of varying radius and color to represent the metal surface atoms and the adsorbate atoms. The study reveals that many surface configurations are possible for a fixed adsorbate coverage (θ) by the movement of the adsorbate atoms in response to surface adsorbate-adsorbate repulsions. The movement of the particles (e.g., particle diffusion) can be seen directly in the model, and this is caused by the user intervention. This has great educational and research value, as one can directly see how the adsorbate atoms reorder on the surface of a metal and therefore study diffusion mechanisms. We calculate the repulsive interaction energy of adsorbates using the sphere-in-contact model and can identify which surface-adsorbed configuration is the lowest energy. We find that at a surface coverage of 1/3 (0.333 ML), the most stable adsorbate configuration places adsorbates at the third nearest neighbor 3-fold hollow sites, forming a hexagonal pattern. We find that this model will be useful in the rational design of catalytic materials and material coatings with new technological applications where long-range ordering of surface adsorbates is essential and adsorbate interactions are mainly repulsive interatomic interactions. Full article

Background: Traditional echocardiographic assessment of aortic stenosis (AS) using aortic valve area (AVA) may overestimate severity due to pressure recovery phenomena, while subclinical myocardial dysfunction remains undetected despite preserved ejection fraction. This study evaluated whether energy loss index (ELI)—which accounts for pressure recovery—demonstrates superior correlation with global longitudinal strain (GLS), a marker of subclinical myocardial dysfunction, compared to conventional AVA-based classification in patients with moderate-to-severe AS and preserved left ventricular ejection fraction (LVEF). Methods: This retrospective single-center study analyzed 149 patients with moderate-to-severe AS (AVA < 1.5 cm²) and LVEF > 50% from 2015 to 2019. Among 97 patients with severe AS by AVA (<1.0 cm²), ELI was calculated using the formula ELI = (AVA × Aa)/(Aa − AVA) ÷ BSA, where Aa represents sinotubular junction cross-sectional area. Patients with ELI ≥ 0.6 cm²/m² were reclassified as moderate AS. Spearman correlation assessed relationships between AVA, ELI, and GLS. Multivariable linear regression models determined independent predictors of myocardial dysfunction, adjusting for age, body surface area, hypertension, LVEF, and mean pressure gradient. Results: ELI reclassified 28 of 97 patients (29%) from severe to moderate AS. Reclassified patients had significantly better myocardial function, with less impaired GLS (−15.0 ± 3.9% vs. −12.1 ± 5.0%, p = 0.013) and higher LVEF (60.1 ± 6.2% vs. 56.5 ± 9.1%, p = 0.017) compared to non-reclassified patients. In the overall cohort, ELI demonstrated stronger correlation with GLS than AVA (r = −0.307, p = 0.0003 vs. r = −0.209, p = 0.0115). Critically, among patients with severe AS by AVA criteria, ELI maintained significant correlation with GLS (r = −0.443, p = 0.0003) while AVA showed no correlation (r = −0.144, p = 0.159). In multivariable analysis, ELI independently predicted GLS (β = 5.847, 95% CI: 2.85–8.84, p = 0.0002; adjusted R² = 0.289), whereas AVA did not (β = 2.234, 95% CI: −1.08 to 5.55, p = 0.188; adjusted R² = 0.234). When both parameters were included simultaneously, only ELI remained significant (p = 0.0024). Conclusions: In this retrospective cohort, ELI-based reclassification identified a subgroup of patients with less severe myocardial dysfunction as measured by GLS and LVEF, and ELI demonstrated superior correlation with subclinical myocardial dysfunction compared to AVA. These findings suggest ELI may provide a more physiologically reflective assessment of hemodynamic burden in AS with preserved LVEF. However, the absence of systematic symptom assessment and clinical outcome data represents critical limitations. Prospective studies with standardized symptom evaluation, longitudinal follow-up, and adjudicated clinical endpoints are required to determine whether ELI-based reclassification improves risk stratification and clinical decision-making before this approach can be recommended for routine practice. Full article

Condition monitoring of rolling bearings is essential for ensuring the reliability of mechanical systems operating under severe or insufficient lubrication conditions. This study proposes a fault diagnosis framework that integrates tribological interpretation of wear phenomena, acoustic emission (AE) signal analysis, and machine learning, based on bearing life tests conducted under dry conditions as an accelerated wear environment to capture damage progression within a practical experimental time. Unlike conventional studies relying on artificially introduced defects, this work focuses on AE signals obtained from bearings in which damage initiates and progresses through actual wear processes. Life tests were conducted using deep groove ball bearings under two radial load conditions. The temporal evolution of the coefficient of friction, AE signals, and surface damage was analyzed. Although the coefficient of friction was the most sensitive indicator of wear progression, its direct measurement is impractical for in-service applications. Frequency-domain analysis revealed that AE counts per second and band-specific AE energy exhibit early changes consistent with the evolution of the friction coefficient. Using these physically interpretable AE features, a fully connected neural network was developed to classify bearing conditions into normal, early-stage damage, and damage progression. The proposed model achieved an average classification accuracy of approximately 85%, demonstrating the effectiveness of AE-based machine learning for bearing fault diagnosis under real wear progression conditions rather than artificial defect scenarios. Full article

The topographic shadow effect can cause surface reflectance distortions in the shadow areas of remote sensing images, particularly in complex mountainous areas. In this study, based on the difference in solar radiation received at the surface of sunlit and shadow areas, we introduced the shadow intensity, vegetation index, and band adjustment factors, and proposed a topographic shadow effect correction (TSEC) method. The method was then tested using eight Landsat 8 OLI scenes under different illumination conditions from two different regions. The results indicate that TSEC effectively corrected the topographic shadow effect. The corrected images exhibited good visual quality without obvious shadow pixels. Importantly, TSEC retained spectral information in sunlit areas while correcting spectral distortion in shadow areas, resulting in strong agreement between spectral curves of shady and sunny slopes. The method demonstrated high stability in normalized difference vegetation index (NDVI) correction, as the difference in NDVI before and after correction was less than 0.07 for the four scenes within the Changjiang study area. Moreover, the TSEC corrected the enhanced vegetation index (EVI) effectively, reducing an initial EVI difference of over 0.35 between the shady and sunny slopes to a maximum of 0.074 for the four scenes within the Wuyi Mountain study area. Relative to four established topographic correction models, the proposed method suppresses the over-correction phenomena typical of self-shadows and minimizes under-correction in cast shadows, resulting in stable overall correction results with few outliers. The TSEC provides a simple and effective method to correct the distorted reflectance in shadow areas using only image and DEM data, which can be adapted to complex mountainous areas and for images with different illumination conditions. Full article

The intensification of large-scale wildfires, driven by climate change, presents a critical threat to agricultural ecosystems, specifically during the vulnerable sowing season in March. Departing from the prevailing focus on urban air quality, this study elucidates the spatiotemporal dynamics of particulate matter (PM) in eight major Korean agricultural regions during the March 2025 wildfires. By employing a Generalized Additive Model (GAM), we characterized the complex non-linear interactions between PM concentrations and meteorological variables. The analysis reveals a substantial elevation in PM levels during the wildfire event relative to the pre-fire baseline. Most notably, the Sangju region experienced the most acute accumulation, with PM-10 and PM-2.5 concentrations surging by 74% and 46%, respectively; this intensification was significantly compounded by topographic trapping and surface inversion phenomena. Furthermore, GAM results identified temperature and relative humidity as the primary determinants of PM retention, whereas wind speed demonstrated a distinct non-linear, U-shaped effect, facilitating particulate resuspension at higher velocities. These findings quantitatively underscore the susceptibility of agricultural environments to wildfire-induced aerosols and highlight the imperative for establishing agriculture-specific monitoring networks and early warning protocols to safeguard crop productivity. Full article

Polymer additive manufacturing (AM) has grown rapidly in the past decade, with material extrusion, vat photopolymerization, powder bed fusion and jetting now widely used for functional polymer parts. The mechanical performance of these parts depends strongly on process parameters such as layer height, build orientation, energy input and post-processing conditions, which motivate the development of predictive models for process–property relationships. Classical approaches based on Taguchi designs, ANOVA and response surface methodology have provided valuable insight, but the potential of modern machine learning (ML) techniques is not yet fully exploited. This review surveys recent work on ML-based prediction of mechanical properties of polymer AM parts using process parameters as inputs. Across the literature, well-tuned artificial neural networks, tree-based ensembles and support vector regression typically achieve prediction errors below about 5–10% for strength and modulus, showing that data-driven surrogates can substantially reduce experimental trial-and-error in process optimization. Ongoing challenges include small datasets, missing standardized error metrics, and limited coverage of non-quasi-static phenomena like fatigue, impact, and environmental degradation. Full article

Due to phenomena, such as refraction, reflection, and light scattering, the three-dimensional (3D) reconstruction of transparent objects with complex geometric symmetry or contours is confronted with the challenges of insufficient extraction of feature points and recognition of contour detail. To solve this challenge, a novel reconstruction method based on multi-cooperative Neural Radiance Fields (NeRF) is proposed in the paper. This method incorporates angular offset fields and local reconstruction fields, explicitly modeling the effects of refraction and reflection during light propagation. The angular offset field simulates the internal refractive deflection within transparent materials, while the localized reconstruction field performs secondary reconstruction in regions affected by specular reflection. This approach effectively captures surface contours of transparent objects and accurately reconstructs scene details. Experimental results demonstrate that our method achieves approximately 10% improvement in reconstruction accuracy compared to traditional neural radiance field techniques, with a PSNR of 25, an increased SSIM of 0.87, and a reduced LPIPS value of 0.365. The proposed method offers a new perspective for reconstructing transparent objects and scenes containing such materials, holding significant theoretical and practical value. Full article

Additive manufacturing has emerged as a key enabling technology for in-space manufacturing, offering the potential to reduce logistics mass, enhance mission autonomy, and support long-duration exploration. The suppression of gravity-driven phenomena fundamentally alters melt pool dynamics, heat transfer, surface-tension-dominated flow, and defect formation, limiting the direct transferability of terrestrial AM process knowledge to space applications. This paper reviews the current understanding of metallic additive manufacturing process physics under reduced gravity, with emphasis on melt pool behavior, dimensional stability, and in situ monitoring constraints. Approaches for qualification and certification are critically examined, including the applicability of existing AM standards, the role of digital twins and model-based verification, and emerging strategies for space-based validation. Enabling technologies such as autonomous and AI-assisted fabrication, compact hardware architectures, and alternative energy sources are discussed in the context of reliable in-space operation. By synthesizing current developments and identifying key limitations and open challenges, the review provides a roadmap for advancing additive manufacturing toward operational readiness, supporting sustainable exploration, in-space infrastructure development, and long-duration human presence beyond low Earth orbit. Full article

Long-distance water conveyance systems often experience free-surface-pressurized flow transitions and air pocket entrapment during filling, which may trigger hazardous phenomena such as air explosions and geysering. Existing models typically lack sufficient predictive accuracy due to oversimplified descriptions of dynamic air exchange and multi-shaft ventilation coupling mechanisms. To resolve this limitation, we propose an enhanced AirSWMM model integrated with a comprehensive ventilation calculation module. The model adopts a unified air pocket formulation and simulates real-time air exchange via predefined ventilation areas along the pipeline. Experimental validation confirms its reliability in predicting key hydraulic parameters, including filling duration, pressure variation, and flow rates. When applied to a prototype project, the model classifies the filling process into four distinct phases based on gas release characteristics and air–water interface movement: initial pressurization, advancing pressurized flow with free venting, system-wide pressurized flow with intermittent venting, and full-pipe flow with terminal intermittent venting. This study provides a robust numerical tool for the safety-oriented management of filling operations in multi-shaft water conveyance systems, delivering practical insights for engineering design and operational optimization. Full article