Search Results (8,476)

Objectives: Idiopathic pulmonary fibrosis (IPF) is a progressive interstitial lung disease with increasing prevalence and mortality. High-resolution computed tomography (HRCT) is routinely performed in IPF assessment and can provide additional quantitative information. However, the prognostic utility of these HRCT-based parameters in IPF remains uncertain. This study aimed to investigate the prognostic value of quantitative HRCT findings in predicting mortality among patients with IPF. Methods: In this retrospective cohort study, 48 patients diagnosed with IPF between 2014 and 2024 were analyzed. Demographics, pulmonary function tests, HRCT findings, and quantitative measurements, including coronary artery calcium (CAC) score; densities of hepatic, paraspinal muscle, and lumbar vertebral bone mineral; and HRCT-derived fibrosis scores, were collected at baseline and after at least two years. The primary outcome was all-cause mortality. Results: The mean age was 66.8 ± 8.5 years; 77.1% were male. During a median follow-up of 63.3 months, 22 patients (45.8%) died, mainly from IPF-related causes (71.4%). Non-survivors had significantly higher HRCT fibrosis scores both at diagnosis (p = 0.006) and at two years (p = 0.002). Fibrosis scores increased significantly over time in non-survivors (p = 0.019). CAC scores rose in both groups, with a greater increase in non-survivors, but their independent prognostic value was limited after adjustment. Multivariable analysis identified male sex (hazard ratio [HR]: 5.46, p = 0.031) and second-year fibrosis score (HR: 1.10, p < 0.001) as independent predictors of mortality. Conclusions: HRCT-derived fibrosis burden is an independent predictor of mortality in IPF, alongside male sex. While other HRCT-based measures showed limited prognostic significance, longitudinal increases in CAC scores suggested potential cardiovascular implications. Full article

Understanding the spatial structure of tree communities is fundamental for evaluating ecological interactions and management dynamics in agroforestry systems. However, the structural complexity and small spatial scale of traditional agroecosystems often limit the use of conventional remote sensing approaches. Recent advances in drone-based photogrammetry offer new opportunities to reconstruct the three-dimensional structure of vegetation at high spatial resolution and to quantify tree-level structural attributes. In this study, we applied aerial photogrammetry from unmanned aerial vehicles (UAVs) to characterize the spatial structure of agroforestry systems in traditional home gardens (THGs) in the Yucatan Peninsula, Mexico. The immediate neighborhood structure of the tree community of 20 THGs distributed along a south–north precipitation gradient was analyzed using two focal species as anchor references: Spondias purpurea and Annona muricata. High-resolution orthomosaics and three-dimensional point cloud models were generated to estimate structural attributes, including tree height, crown area, crown surface area, and canopy volume, which were combined with field measurements of diameter at breast height. Spatial indices describing aggregation, dominance, and neighborhood diversity were calculated to evaluate tree spatial organization and potential interaction patterns. The UAV-derived structural metrics revealed significant differences in canopy architecture across regions and between focal species. Regardless of the focal species, trees in the southern region exhibited greater height, crown diameter, and canopy volume than those in the northern region. Moreover, the spatial arrangement of tree communities also differed depending on which focal species was considered as the anchor, suggesting contrasting strategies of canopy dominance and spatial coexistence. Finally, our results validate the use of drone-based photogrammetry as an effective approach for capturing fine-scale spatial structure in complex agroforestry systems. By enabling detailed three-dimensional reconstruction of tree canopies, UAV remote sensing offers an affordable, simple approach to investigate neighborhood interactions, management effects, and structural dynamics in traditional agroecosystems that are difficult to assess using conventional field- or satellite-based methods. Full article

Distributed fiber optic sensing (DFOS) has emerged as a transformative technology for structural health monitoring (SHM) of railway infrastructure, offering continuous, high-resolution measurements along extended optical fiber lengths, capabilities that conventional point sensors such as strain gauges and accelerometers cannot match. This review critically examines DFOS technology and its railway SHM applications, covering system components, interrogator units, optical fiber cables, and data acquisition systems, alongside the three principal scattering mechanisms: Rayleigh, Brillouin, and Raman, each offering distinct trade-offs in spatial resolution, sensing range, and measurand sensitivity. Field applications across track and sleeper monitoring, bridge health evaluation, tunnel lining assessment, and embankment stability are reviewed and critically compared. The integration of artificial intelligence (AI) and machine learning (ML) with DFOS data streams is discussed, demonstrating detection accuracy exceeding 97% in recent studies. Its main application rail embankment monitoring is discussed. Key challenges are identified, including high interrogator costs, large data volumes, installation complexity in retrofit scenarios, and environmental noise under operational train speeds. Future research priorities include lower-cost interrogation hardware, automated signal processing pipelines, digital twin integration, and standardized performance frameworks to accelerate large-scale adoption across railway networks worldwide. Full article

Collision statistics provide a finite-resolution view of information by measuring how often independent samples fall on the same state and form the basis of integer-order Rényi entropies. Here, we use low-order Rényi entropies to characterize finite-resolution approximations to Shannon entropy and mutual information. Specifically, we determine what population information is captured by finite collision moments, we quantify how the resulting targets differ from their Shannon counterparts, and we analyze how accurately they can be estimated from finite samples. We use the interpolation remainder to identify structural approximation error induced by extrapolating from integer-order Rényi entropies to the Shannon point. We separate this deterministic error from finite-sample estimation error: increasing sample size improves estimation of a finite-resolution target but does not eliminate its deterministic difference from Shannon entropy or mutual information. Finally, we show that finite collision moments do not generally identify Shannon entropy, and that increasing collision order shifts sensitivity toward high-probability events. Our numerical experiments illustrate the approximation–estimation trade-off and evaluate collision-based approximations alongside plug-in and Miller–Madow estimators. Together, these results provide a principled way to use low-order coincidence structure as finite-resolution information, while making explicit what finite collision moments can and cannot reveal about Shannon entropy and mutual information. Full article

Recent advances in Earth observation and machine learning have significantly enhanced the capacity to monitor agricultural systems and terrestrial vegetation across spatial and temporal scales. This editorial synthesizes the contributions of eleven studies published in the Special Issue ‘Machine Learning for Applications in Agriculture and Vegetation Using Remote Sensing’. These contributions highlight emerging methodological trends, as well as persistent challenges in remote sensing for agriculture and vegetation measurement and monitoring, and reflect the growing dominance of deep learning in high-resolution mapping and segmentation. An increasing importance of multi-sensor data fusion, integrating multi- and hyperspectral satellites, UAV, and environmental data, is found. Deep learning is emerging as an effective approach for retrieving key biophysical parameters such as biomass, crop height, and yield. The collected studies also emphasize the critical role of sensor characteristics and scale, particularly the trade-offs between spectral, spatial, and temporal resolution in vegetation analysis. Despite notable progress, several limitations remain. Model transferability across regions and sensors is still constrained and multi-source data integration often lacks standardized frameworks. Empirical approaches still dominate the retrieval of biophysical variables, limiting robustness and physical interpretability. The contributions also reveal a persistent gap between high-resolution, small-scale analyses and their scalability to regional or global applications. Therefore, this editorial argues for a transition towards hybrid modeling approaches that combine physical knowledge with data-driven machine learning methods, the adoption of formal data assimilation frameworks for multi-source integration, and the development of scalable and uncertainty-aware workflows. The broader scientific context of the contributions is given by providing a critical perspective on the current state of the field and outlining the key research directions necessary to advance remote sensing in agriculture and ecosystem monitoring. Full article

This study investigates the electrochemical properties of polymer-based microelectrode arrays (pMEAs) and their performance in measuring oxygen (O₂) and hydrogen peroxide (H₂O₂). Morphological characterization by scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS) and X-ray diffraction (XRD) revealed a uniform, fine-grained platinum surface with nanoscale roughness, consistent with the Ti/Pt/Au/Pt multilayer stack architecture. The electrochemical behavior of the pMEAs was assessed using cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS), which demonstrated favorable responses for both O₂ reduction and H₂O₂ oxidation, together with low impedance (41.1 kΩ at 1 kHz). For O₂ detection, amperometric measurements at −0.6 V vs. Ag/AgCl indicated a sensitivity of −0.25 ± 0.04 nA μM⁻¹ and a detection limit of 5.4 ± 1.4 nM. For H₂O₂ detection, application of +0.7 V vs. Ag/AgCl resulted in a sensitivity of 88.13 ± 7.61 nA mM⁻¹ and a detection limit of 41.9 ± 5.6 nM. Selectivity evaluation showed effective interferent exclusion following m-phenylenediamine electrodeposition, without compromising analytical performance. Overall, these findings indicate the suitability of pMEAs for real-time, in vivo monitoring of O₂ and H₂O₂ in brain tissue with high spatial and temporal resolution, supporting applications in oxidative stress research and neurometabolic sensing. Full article

Terahertz (THz) waves offer unique advantages, including non-contact operation, high penetration capability, and high resolution, making them particularly well-suited for the non-destructive thickness measurement of film-structured materials. In reflective terahertz time-domain spectroscopy (THz-TDS), thickness measurement approaches are generally classified into three categories: optimization-based methods that rely on theoretical models, time-of-flight (ToF), and machine learning. Model-based optimization techniques require precise knowledge of the optical parameters and structural configuration of each layer; however, they often suffer from slow convergence and are prone to becoming trapped in local optima. In contrast, ToF-based methods determine thickness by calculating the time delay between echo pulses reflected from different interfaces, yet their applicability is limited when the film thickness is extremely small. Machine learning, especially deep learning, enables the establishment of a direct, data-driven mapping between THz waveforms (or their extracted features) and the target thickness. Such approaches offer rapid inference, strong robustness to noise, and good adaptability to thin or structurally complex films, although their accuracy remains dependent on the quality of training data and the generalization capability of the model. In this study, high-fidelity THz waveform data generated via finite-difference time-domain (FDTD) simulations are utilized to conduct a comparative investigation into the film thickness prediction performance of several representative machine learning algorithms, including Back Propagation (BP) neural networks, Support Vector Machines (SVM), Random Forests (RF), Extreme Learning Machines (ELM), K-Nearest Neighbors (KNN), and Partial Least Squares (PLS) regression. The results indicate that, in terms of prediction error, the overall ranking of algorithmic performance from best to worst is: PLS > RF > SVM > BP > ELM > KNN. These findings provide valuable guidance for the future application of machine learning-assisted THz-TDS in precise film thickness measurement. Full article

The analysis of process signals is a key method for gaining experimental insight into the underlying layer formation mechanisms in plasma electrolytic oxidation (PEO). This is made possible by the simultaneous measurement of electrical and optical process signals with high temporal resolution. However, according to the current state of the art, the interaction between these signals is primarily discussed in qualitative terms. Therefore, this article presents a robust methodology for analysing the current signal, which makes it possible to categorise the charge electro-chemical and plasma-chemical dominated subprocesses and to quantify their respective contributions. The evaluation is performed by taking additional process signals into account. The experimental setup for measuring process voltage, current, and photovoltage, as well as the measurement routine, are briefly described. This is followed by a detailed description of the numerical procedure. This includes the application of fundamental mathematical methods to the time-discrete measurement data, the automated selection of the pulse segment to be examined and the identification of discharge initiation to determine the interval boundaries of the electro- and plasma-chemically dominated pulse subsegments. The description of the routine is primarily intended for experimental scientists and is meant to provide them with a tool for extracting additional information from their process data. These can then be used to better understand electro-chemical side reactions and parasitic subprocesses in PEO. Full article

Hyperspectral infrared sounders provide valuable observations for numerical weather prediction (NWP), but their native nadir spatial resolution of approximately 12–16 km is coarser than the approximately 4 km grid spacing commonly used in convection-permitting regional forecasting systems. To enhance the spatial resolution of these observations toward this scale, we propose the Channel-Decoupling and Geographic-Prior Fusion Network (CDGP-Net), an unsupervised hyperspectral–multispectral fusion framework that reconstructs 4 km high-spatial-resolution hyperspectral radiances by fusing the FengYun-3D (FY-3D) Hyperspectral Infrared Atmospheric Sounder (HIRAS) data with co-platform Medium Resolution Spectral Imager II (MERSI-II) imagery while preserving the original spectral sampling. To adapt hyperspectral–multispectral fusion to infrared sounder data, CDGP-Net incorporates two components: a self-reconstruction and spectral-degradation channel-decoupling (SDCD) design, which allows physically related non-overlapping MERSI-II infrared information to be used as an auxiliary input while keeping the spectral degradation physically consistent; and a reconstruction-domain geographic-prior regularization (RGPR) scheme, which constrains the reconstructed radiances in both geographic space and spectral shape. Because true high-resolution observations are unavailable, we further introduce a radiative-transfer-anchored evaluation (RTAE) scheme that uses the line-by-line radiative transfer model (LBLRTM) simulations driven by reanalysis and forecast atmospheric fields as independent physical references. For the selected FY-3D overpass cases, the evaluation using Ref-HR as the high-resolution physical reference shows that CDGP-Net improves the peak signal-to-noise ratio (PSNR) by 3.8 dB and reduces the spectral angle mapper (SAM) and erreur relative globale adimensionnelle de synthèse (ERGAS) by 64.1% and 54.6%, respectively, compared with the unmixing baseline. Under the same evaluation conditions, relative to geographic interpolation, it improves the structural similarity index measure (SSIM) by 16.4% and reduces ERGAS by 8.8%, with the clearest advantages in partial-cloud and coastal transition scenes. Full article

Background: High-resolution ultrasound is increasingly used as a complementary tool in carpal tunnel syndrome (CTS), but the value of median nerve cross-sectional area (CSA) for severity stratification remains uncertain. This study evaluated whether median nerve CSA measured at the pisiform level can distinguish severe from moderate CTS classified according to the Bland grading scale. Methods: This prospective cross-sectional study included 72 participants: moderate CTS (n = 34), severe CTS (n = 24), and healthy controls (n = 14). Nerve conduction studies, abductor pollicis brevis needle electromyography, and ultrasound examinations were performed on the same day. CSA was measured three times by a blinded examiner, and the mean value was used for analysis. Correlation, age-adjusted linear regression, and receiver operating characteristic curve analyses were performed. Results: Median nerve CSA increased progressively from controls to moderate and severe CTS groups (8.0 ± 1.1, 16.5 ± 4.6, and 19.8 ± 3.4 mm², respectively; p < 0.001), with significant pairwise differences. Intraobserver and interobserver reliability were excellent (ICC = 0.95 and 0.91, respectively). CSA correlated positively with distal motor and sensory latencies and inversely with compound muscle action potential and sensory nerve action potential amplitudes. CSA also showed a weak positive association with neurogenic motor unit action potentials. In age-adjusted regression analysis, distal motor latency remained independently associated with CSA (B = 0.873, p = 0.002). A CSA cut-off of 18.5 mm² distinguished severe from moderate CTS with an area under the curve (AUC) of 0.728, sensitivity of 66.67%, and specificity of 79.41%. Conclusions: Median nerve CSA measured at the pisiform level may provide useful adjunctive structural information for CTS severity assessment and may show moderate discriminatory performance for differentiating severe from moderate CTS. CSA should be interpreted in conjunction with clinical and electrodiagnostic findings rather than as a stand-alone severity marker. Full article

We present the results of tests conducted with a state-of-the-art drone-based multi-channel magnetometer system (SENSYS MagDrone R4) developed for efficient high-resolution near-surface surveys. The primary aim is to advance the application of this technology in proximal sensing and to identify acquisition strategies capable of achieving data quality comparable to that of established ground-based magnetometer surveys. The system is evaluated under varying operational conditions, with particular emphasis on the influence of flight altitude, heading, velocity, and UAV platform characteristics on magnetic data quality. Signal properties are analyzed using power spectral methods to identify and quantify platform- and survey-related sources of interference. The results reveal the noise sources and amplifiers affecting UAV-based magnetic measurements and demonstrate how survey design can substantially increase the signal-to-noise ratio. Based on these findings, practical recommendations for data acquisition and processing are proposed, contributing to the development of best-practice guidelines for high-resolution archeological, explosive ordnance (EO), and other near-surface magnetic survey applications. Full article

Scaphopod molluscs are marine, infaunal, cosmopolitan animals. They encase their soft tissue with an aragonitic tusk-shaped shell that has at its two ends an orifice, the aperture and the apex. We investigated the shell of the dentaliid species Fissidentalium metivieri and Antalis weinkauffi and characterized shell crystal organization with electron backscatter diffraction (EBSD) and laser confocal and scanning electron microscopies. Based on crystal size, morphology, organization and growth-line spacing, we distinguish five different crystal arrangement motifs in the investigated shells. We find two slightly different microstructures for the shell proper, two microstructures for the attachment of muscles to the shell and one microstructure for a secondary hard tissue growth product, secreted at the apical orifice. Crystal organization motifs are crossed-lamellar, dendritic and prismatic. Crystal textures are crossed-lamellar, axial-like and crossed-lamellar-like. Crystal organization with a crossed arrangement is utilized for shell formation by representatives of many Ca-carbonate shell-secreting mollusc classes/subclasses: Scaphopoda, Gastropoda, Bivalvia, Polyplacophora (crossed-lamellar), and Patellogastropoda (crossed-foliated). Based on structural–crystallographic attributes, we highlight a basic shell structure that is broadly similar for the species of these mollusc classes/subclasses. However, this basic crystal arrangement motif is significantly modulated by the specific crystal organization that is inherent for a particular mollusc class/subclass. Full article

Mitochondrial DNA (mtDNA) heteroplasmy, which is the coexistence of wild-type and mutant mtDNA variants within the same cell, plays a critical role in modulating cellular phenotypes, disease severity, and penetrance. Bulk RNA sequencing cannot detect cell-to-cell heteroplasmy variability, limiting our understanding of the pathological mechanisms of mtDNA variants. In this study, we leveraged single-cell RNA sequencing (scRNA-seq) combined with a robust bioinformatics pipeline to characterize mtDNA heteroplasmy. We employed four fibroblast lines from patients harboring heteroplasmic mtDNA pathogenic variants in genes encoding respiratory complex I subunits. While RNA heteroplasmy corresponded to DNA-based measurements at the bulk level, single-cell analysis uncovered a diverged distribution in three out of four lines: most cells had near-homoplasmic (wild-type or mutant) mtDNA, with few cells showing intermediate levels. Furthermore, we found that high mutation levels correlate with transcriptional profile changes, although these responses were highly sample-specific, suggesting that the nuclear background and cellular context critically influence mitochondrial dysfunction and compensatory mechanisms. Our findings highlight the potential of single-cell technologies to better understand the complex link between mtDNA genetic diversity and mitochondrial phenotypic variability and to study crucial aspects of mitochondrial biology and pathology, such as clonal dynamics, at single-cell resolution. Full article

Measuring the water table level is a critical factor in irrigated agriculture in arid regions, as it can significantly influence the exchange of water and nutrients with crops. This work presents the design, implementation, and field validation of an open-source, solar-powered IoT device for autonomous groundwater level monitoring, combining long-range low-power LoRa communication, a non-contact pressure-based level sensor using the trapped-air capillary method, and an efficient power management stage that seamlessly switches between solar and battery power. Unlike existing commercial leveloggers, which are costly and lack integrated wireless telemetry and solar-based autonomy, the proposed platform is presented as a fully open-source, low-cost alternative purpose-built for unattended deployment in areas without grid power or cellular coverage. The system was validated through a multi-day field trial and dedicated communication tests, demonstrating a stable power conversion efficiency of 84–90%, a five-day autonomous operation without any deep-discharge event, high linearity ($R^2$ = 0.9998) of the level module over a 0–2 m range with a resolution of approximately 1.94 mm per ADC count, and a reliable LoRa link of up to 8.51 km in an urban/suburban environment despite non-line-of-sight conditions. With an estimated hardware cost of approximately $100 USD per unit, the device represents a low-cost, low-maintenance tool capable of generating knowledge about water resources to optimize irrigation and crop management in the face of climate change. Full article

Cover cracking induced by steel corrosion is a critical issue governing the durability degradation of reinforced concrete structures in marine environments. The crack initiation and propagation processes dominated by non-uniform rust expansion stress fields urgently require high-resolution continuous monitoring techniques. In this study, based on the principle of Rayleigh backscattering, distributed optical fibers were embedded along the upper surface of specimens to conduct in situ monitoring of surface strain in concrete. The effects of specimen length, biochar content, cover thickness, and rebar diameter were systematically investigated. The results indicate that the surface strain evolution follows a two-stage pattern—a slow growth stage followed by a rapid rise stage—corresponding respectively to the early-stage filling of interfacial pores and stress accumulation, and the later-stage propagation of macroscopic cracks. Increasing specimen length significantly amplifies the spatiotemporal non-uniformity of strain, characterized by “locally high peak strains but low overall mean values,” with the onset time of strain surges differing by more than 50 h across different cross-sections. The incorporation of 0.5% biochar reduces the average strain by approximately 17% and delays crack initiation to 260 h. Increasing cover thickness from 25 mm to 40 mm exhibits the most pronounced inhibitory effect, achieving a 39% reduction in strain and delaying crack initiation to 320 h, primarily attributed to the extended chloride transport path and enhanced hoop confinement stiffness. Reducing rebar diameter from 20 mm to 12 mm decreases the peak strain to 79% of that of the standard specimen, owing to reduced rust product volume and increased relative cover thickness. The macro-cell effect driven by chloride concentration gradient transition zones is identified as a key factor governing crack initiation locations. Theoretical crack widths derived from strain integration of optical fiber data are slightly lower than measured values, yet the overall trends remain consistent. This study provides a quantitative basis for continuous monitoring and durability assessment of corrosion-induced cracking in marine environments. Full article