Search Results (182)

This study presents U-Net deep learning of total electron content (TEC) obtained from Global Ionosphere Maps (GIMs) to forecast ionospheric TEC over the African 0–40° N latitude sector during geomagnetic storms which have occurred between 2011 and 2024. Before being utilized in the deep learning procedure, the GIM-TEC data were improved by assimilating ground-based vertical TEC (VTEC) observations from available Global Navigation Satellite System (GNSS) receiver stations. The U-Net one-hour-ahead prediction of TEC was examined during the intense geomagnetic storm of May 2024. Additionally, the model’s accuracy and reliability were evaluated through quantitative comparison with established climatological models, including IRI-2020 and AfriTEC storm time models. The results indicate that the integration of data assimilation with the deep learning framework yields TEC estimates that closely agree with observations, achieving a RMSE of approximately 5 TECU. On the other hand, the IRI-2020 model exhibits substantially larger errors, with RMSE ~10–17 TECU, while the AfriTEC model shows the poorest performance, with RMSE reaching approximately 15–22 TECU. Further, the U-Net was validated using two equatorial and mid-latitude GNSS stations whose data were excluded from the assimilation process, achieving RMSE values of 4.44 and 6.75 TECU and correlation coefficients of 0.93 and 0.97, confirming the model forecasting capability for reproducing ionospheric TEC variability. These results establish the model as a precise, robust tool for TEC prediction in regions with sparse GPS coverage that is crucial for ionospheric monitoring and space weather applications. Full article

HTD1265 is a newly identified antifungal compound that displays potent activity against Candida krusei, a clinically challenging non-albicans species. To elucidate its mechanism of action, we applied an integrative phenotypic approach combining high-resolution morphological profiling, pathway inference, and genetic validation in Saccharomyces cerevisiae. Morphological signature extraction revealed a characteristic defect in nuclear positioning upon HTD1265 treatment. Integration of nuclear positioning traits with global morphological similarity highlighted 36 genes enriched for the Gene Ontology term “tubulin complex assembly.” Consistent with this prediction, HTD1265 impaired mitotic spindle elongation without directly inhibiting tubulin polymerization. HTD1265 further induced hallmarks of GimC (prefoldin) deficiency, including aberrant chitin accumulation, actin disorganization, and nuclear mispositioning, and caused hypersensitivity in GimC subunit mutants. These converging observations suggest that HTD1265 exerts antifungal activity by disrupting GimC-dependent cellular processes rather than by directly targeting tubulin. Our findings highlight GimC-dependent cytoskeletal and cell wall regulatory processes as a critical vulnerability for fungal growth and position HTD1265 as a functional tool for dissecting this pathway. Full article

Ionospheric delay errors significantly reduce the positioning accuracy of global navigation satellite systems (GNSSs), whereas precise ionospheric modeling can effectively mitigate this issue. The ionosphere exhibits large-scale symmetry, and spherical harmonics (SHs) can effectively describe this property due to their rotational symmetry on the sphere. However, mathematical fitting models such as spherical harmonic functions and polynomial models encounter boundary inaccuracies caused by edge effects. To address this problem, we developed a spherical harmonic–radial basis function (SH-RBF) hybrid method based on the integration of spherical harmonics and radial basis function interpolation techniques. This method leverages the global symmetry of spherical harmonics and utilizes the local adaptability of radial basis functions to correct regional distortions. Validation using European GNSS data during both geomagnetically quiet and active periods, in comparison with the CODE global ionospheric map (GIM), demonstrates that the modeling accuracy of spherical harmonics surpasses that of POLY during geomagnetically quiet periods. Compared to spherical harmonics, SH-RBF improves overall modeling accuracy by 8.87–27.27% and enhances accuracy in edge regions by 34.16–83.91%. During geomagnetically active periods, the SH-RBF method also achieves notable improvements. This study confirms that SH-RBF is a reliable technique for significantly reducing edge effects in regional ionospheric modeling. Full article

Altitudinal gradients offer powerful natural frameworks to investigate how environmental factors shape biodiversity, especially on young oceanic volcanic islands where short spatial distances encompass sharp climatic transitions. This study documents bryophyte diversity and examines how elevation, substrate, and environmental variables influence the structure of bryophyte communities on Flores Island (Azores). Across five sites and 385 microplots, 89 species from 37 families were recorded, with liverworts predominating (liverwort-to-moss ratio of 1.41). Species richness and abundance followed a unimodal pattern, peaking at mid-elevations (400–600 m a.s.l.), where humid and thermally stable conditions favor the coexistence of lowland and montane taxa. Even modest altitudinal shifts corresponded to pronounced turnover in community composition, revealing strong ecological filtering along the gradient. Substrate type further influenced diversity patterns, with liverworts dominating epiphytic and lignicolous habitats, while mosses were more diverse on terricolous and rupicolous substrates. The presence of several Azorean and Macaronesian endemics, including threatened taxa, highlights the conservation importance of mid-elevation habitats. Overall, these results show that fine-scale altitudinal variation generates substantial ecological differentiation, underscoring the role of montane forests as refugia for hygrophilous and endemic bryophytes and as sensitive indicators of environmental change in island ecosystems. Full article

Gastrointestinal microbiota (GIM) play a crucial role in host physiology and are modulated by host biology, environmental conditions, and temporal dynamics. The GIM of two types of fishes, the redbreast sunfish (Lepomis auritus) and the bullhead catfish (Ameiurus spp.), from three streams over two seasons were sampled for host health (hepatosomatic index, Fulton’s condition factor), age, and additional environmental metadata. A total of 56 of these were fully analyzed using 16S rRNA amplicon sequencing and QIIME2. Specific taxonomic lineages were identified as significant with respect to observed differences between variables, including season, stream, and host taxonomic affiliation. The relative abundance of bacterial phyla varied significantly based on host type and between the three sites. However, the most significant effects for both relative abundance and alpha diversity metrics were seen when combining variables of site and season or host and season. Principal Component Analysis using weighted and unweighted Unifrac indicated the primacy of season in beta diversity analyses. Analysis of Compositions of Microbiomes (ANCOM) to identify taxa responsible for these differences revealed distinct amplicon sequence variants enriched by season, stream, host taxonomy, and host age. The larger picture emerging from these data suggests that there is a complex interplay between the host, season, and environment that shapes the structure of fish microbiota and associated host health. Full article

A series of multi-parameter, multi-layer observations was conducted to study possible electromagnetic precursors associated with the M 6.7 earthquake that struck Iburi, Hokkaido, Japan, at 18:07:59 UT on 5 September 2018. The most significant observation is seismogenic lower-ionospheric perturbations in the propagation anomalies of sub-ionospheric VLF/LF signals recorded in Japan and Russia. Other substantial observations include the GIM-TEC irregularities, the intensification of stratospheric atmospheric gravity waves (AGWs), and the satellite and ground monitoring of air temperature (T), relative humidity (RH), atmospheric chemical potential (ACP), and surface latent heat flux (SLHF). We have found that there were very remarkable VLF/LF anomalies indicative of lower-ionospheric perturbations observed on 4 and 5 September just before the EQ date and even after it from the observations in Japan and Russia. In particular, the anomaly was detected for a particular propagation path from the JJY transmitter (Fukushima) to a VLF station at Wakkanai one day before the EQ, i.e., on 4 September, and is objectively confirmed by machine/deep learning analysis. An anomaly in TEC occurred only on 5 September, but it is unclear whether it is related to a pre-EQ effect or a minor geomagnetic storm. We attempted to determine whether any seismo-related atmospheric gravity wave (AGW) activity occurred in the stratosphere. Although numerous anomalies were detected, they are most likely associated with convective weather phenomena, including a typhoon. Finally, the Earth’s surface parameters based on satellite monitoring seem to indicate some anomalies from 29 August to 3, 4, and 5 September, a few days prior to EQ data, but the ground-based observation close to the EQ epicenter has indicated a clear T/RH and ACP on 2 September with fair weather, but no significant data on subsequent days because of severe meteorological activities. By integrating multi-layer observations, the LAIC (lithosphere–atmosphere–ionosphere coupling) process for the Hokkaido earthquake appears to follow a slow diffusion-type channel, where ionospheric perturbations arise a few days after ground thermal anomalies. This study also provides integrated evidence linking concurrent lower-ionospheric, atmospheric, and surface thermal anomalies, emphasizing the diagnostic value of such multi-parameter observations in understanding EQ-associated precursor signatures. Full article

This study investigates the ionospheric responses to two successive geomagnetic storms that occurred on 7–8 and 10–11 October 2024 over the Indian equatorial and low-latitude sector. Using GNSS-derived vertical total electron content (VTEC) measurements and the Global Ionosphere Map (GIM)-derived VTEC variation, supported by O/N₂ ratio variations, equatorial electrojet (EEJ) estimates, and modeled equatorial electric fields from the Prompt Penetration Equatorial Electric Field Model (PPEEFM), the distinct mechanisms driving storm-time ionospheric variability were identified. The 7–8 October storm produced a strong positive phase in the morning sector, with VTEC enhancements exceeding 100 TECU, followed by sharp afternoon depletions. This short-lived response was dominated by prompt penetration electric fields (PPEFs), subsequently suppressed by disturbance dynamo electric fields (DDEFs) and storm-induced compositional changes. In contrast, the 10–11 October storm generated a more complex and prolonged response, including sustained nighttime enhancements, suppression of early morning peaks, and strong afternoon depletions persisting into the recovery phase. This behavior was mainly controlled by DDEFs and significant reductions in O/N₂, consistent with long-lasting negative storm effects. EEJ variability further confirmed the interplay of PPEF and DDEF drivers during both events. The results highlight that even storms of comparable intensity can produce fundamentally different ionospheric outcomes depending on the dominance of electrodynamic versus thermospheric processes. These findings provide new insights into storm-time ionospheric variability over the Indian sector and are crucial for improving space weather prediction and GNSS-based applications in low-latitude regions. Full article

This study investigates the synthesis and characterization of amorphous carbon (a-C) layers using three magnetron sputtering (MS) techniques: Pulsed MS (PMS), Gas Injection MS (GIMS), and High Power GIMS (HiPGIMS). The primary objective was to understand how these methods influence the sp³/sp² hybridization ratio, a critical parameter for tailoring the properties of amorphous carbon. Plasma diagnostics via Optical Emission Spectroscopy revealed distinct discharge characteristics, with HiPGIMS exhibiting the highest current density and plasma ionization. Structural and compositional analyses using Raman Spectroscopy and X-ray Photoelectron Spectroscopy (XPS) demonstrated a clear trend: sp³ content increased significantly from PMS to GIMS to HiPGIMS, reaching up to 50% (Raman) and 39% (XPS). This enhancement is attributed to the higher plasma density and more energetic ion bombardment in HiPGIMS, which promotes the formation of sp³ bonds. Ellipsometric spectroscopy further supported these findings, showing that HiPGIMS produced layers with the widest bandgap, indicative of higher sp³ content. The research highlights the effectiveness of advanced MS techniques, particularly HiPGIMS, in precisely controlling the sp³/sp² ratio and thereby the electrical, optical, and mechanical properties of a-C layers for various applications. Full article

This paper analyzes a finite-capacity $GI/M/2/N$ queue with two heterogeneous servers operating under a multiple working-vacation policy, Bernoulli feedback, and customer impatience. Using the supplementary-variable technique in tandem with a tailored recursive scheme, we derive the stationary distributions of the system size as observed at pre-arrival instants and at arbitrary epochs. From these, we obtain explicit expressions for key performance metrics, including blocking probability, average reneging rate, mean queue length, mean sojourn time, throughput, and server utilizations. We then embed these metrics in an economic cost function and determine service-rate settings that minimize the total expected cost via the Bat Algorithm. Numerical experiments implemented in R validate the analysis and quantify the managerial impact of the vacation, feedback, and impatience parameters through sensitivity studies. The framework accommodates general renewal arrivals ($GI$), thereby extending classical ($M/M/2/N$) results to more realistic input processes while preserving computational tractability. Beyond methodological interest, the results yield actionable design guidance: (i) they separate Palm and time-stationary viewpoints cleanly under non-Poisson input, (ii) they retain heterogeneity throughout all formulas, and (iii) they provide a cost–optimization pipeline that can be deployed with routine numerical effort. Methodologically, we (i) characterize the generator of the augmented piecewise–deterministic Markov process and prove the existence/uniqueness of the stationary law on the finite state space, (ii) derive an explicit Palm–time conversion formula valid for non-Poisson input, (iii) show that the boundary-value recursion for the Laplace–Stieltjes transforms runs in linear time $\mathcal{O}\left(N\right)$ and is numerically stable, and (iv) provide influence-function (IPA) sensitivities of performance metrics with respect to $({\mu }_1,{\mu }_2,\nu ,\alpha ,\varphi ,\beta )$. Full article

Background/Objectives: Gastric intestinal metaplasia (GIM) is a recognized premalignant condition for gastric cancer (GC), but long-term outcomes and predictors of progression remain incompletely understood. This study aimed to evaluate the progression of GIM and identify factors associated with malignant transformation. Methods: In this retrospective single-center study, 1204 adult patients with histologically confirmed GIM and at least 12 months of follow-up after esophagogastroduodenoscopy (EGD) were analyzed. Clinical and pathological variables, including GIM extent, Helicobacter pylori status, family history of GC, demographic factors, and residence in endemic regions, were assessed. Patients were stratified into high- and low-risk groups according to established criteria, and progression to GC or other neoplasms was recorded. Results: During a mean follow-up of 38.6 months, 49.1% of patients had no detectable GIM at the end of follow-up, 48.7% remained unchanged, and 2.2% showed disease progression. Among progressed cases, adenocarcinoma accounted for 66.7%, dysplasia for 29.6%, and SCC for 3.7%. Progression was significantly more common among males, older patients, and those with antrum + corpus involvement. The overall progression rate from GIM to adenocarcinoma was 1.5% (approximately 0.45% per patient-year). No significant difference in progression or survival was observed between high- and low-risk groups. Conclusions: The long-term malignant transformation rate of GIM is low. Male sex and extensive gastric involvement were associated with higher progression rates, while H. pylori was not predictive of malignant transformation. These findings support individualized surveillance strategies for patients with GIM, while routine surveillance of antrum-limited GIM may provide minimal benefit but increase healthcare burden. Full article

Global navigation satellite system (GNSS) differential code bias (DCB) and topside ionosphere vertical electron content (VEC) can be estimated using onboard data from low-earth-orbit (LEO) satellites. These satellites provide the potential to make up for the lack of ground-based stations in the oceanic and polar regions and establish a high-precision global ionosphere model. In order to study the influences of different LEO-topside VEC processing methods on estimates, we creatively analyzed and compared the results and accuracy of the DCBs and LEO-topside VEC estimates using two topside VEC solutions—the SH-topside VEC (spherical harmonic-topside vertical electron content) and EP-topside VEC (epoch parameter-topside vertical electron content) methods. Some conclusions are drawn as follows. (1) Using GRACE-A data (400 km in 2016), the monthly stabilities (STDs) of GPS satellite DCBs and LEO receiver DCBs using the EP-topside VEC method are better than those using the SH-topside VEC method. For JASON-2 data (1350 km), the STD results of GPS DCBs using the SH-topside VEC method are slightly superior to those using the EP-topside VEC method, and LEO DCBs using the two methods have similar STD results. However, the root mean square (RMS) results for GPS DCBs using the SH-topside VEC model relative to the Center for Orbit Determination in Europe (CODE) products are slightly superior to those using the EP-topside VEC method. (2) The peak ranges of the actual GRACE-A-topside VEC results using the SH-topside VEC and EP-topside VEC methods are within 42 and 35 TECU, respectively, while the peak ranges of the JASON-2-topside VEC results are both within 6 TECU. Additionally, only the SH-topside VEC model results are displayed due to the EP-topside VEC method not modeling VEC. Due to the difference in orbital altitude, the results and distributions of the GRACE-topside VECs differ from those of the JASON-topside VECs, with the former being more consistent with the ground-based results, indicating that there may be different height structures in the LEO-topside VECs. In addition, we applied the IRI-GIM (International Reference Ionosphere model–Global Ionosphere Map) method to compare the LEO-based topside VEC results, which indicate that the accuracy of GRACE-A-topside VEC using the EP-topside VEC method is better than that using the SH-topside VEC method, whereas for JASON-2, the two methods have similar accuracy. Meanwhile, we note that the temporal and spatial resolutions of the SH-topside VEC method are higher than those of the EP-topside VEC method, and the former has a wide range of usability and predictive characteristics. The latter seems to correspond to the single-epoch VEC mean of the former to some extent. Full article

This research introduces a hybrid framework that integrates stochastic modeling and machine learning for predicting postprandial glucose levels in individuals with Type 1 Diabetes (T1D). The primary aim is to enhance the accuracy of glucose predictions by merging a biophysical Glucose–Insulin–Meal (GIM) model with advanced machine learning techniques. This framework is tailored to utilize the Kaggle BRIST1D dataset, which comprises real-world data from continuous glucose monitoring (CGM), insulin administration, and meal intake records. The methodology employs the GIM model as a physiological prior to generate simulated glucose and insulin trajectories, which are then utilized as input features for the machine learning (ML) component. For this component, the study leverages the Light Gradient Boosting Machine (LightGBM) due to its efficiency and strong performance with tabular data, while Long Short-Term Memory (LSTM) networks are applied to capture temporal dependencies. Additionally, Bayesian regression is integrated to assess prediction uncertainty. A key advancement of this research is the transition from a deterministic GIM formulation to a stochastic differential equation (SDE) framework, which allows the model to represent the probabilistic range of physiological responses and improves uncertainty management when working with real-world data. The findings reveal that this hybrid methodology enhances both the precision and applicability of glucose predictions by integrating the physiological insights of Glucose Interaction Models (GIM) with the flexibility of data-driven machine learning techniques to accommodate real-world variability. This innovative framework facilitates the creation of robust, transparent, and personalized decision-support systems aimed at improving diabetes management. Full article

Global Navigation Satellite System (GNSS) spoofing, which manipulates PVT solutions through false measurements, increasingly threatens GNSS reliability and user safety. However, most existing simulator-based spoofers, constrained by their inability to access real-time ionospheric information (e.g., Global Ionosphere Maps, GIMs) from external sources, struggle to replicate authentic total electron content (TEC) along each signal propagation path accurately and in a timely manner. In contrast, widespread dual-frequency (DF) receivers with access to the internet can validate local TEC measurements against external references, establishing a pivotal spoofing detection distinction. Here, we propose an Ionospheric Information-Assisted Spoofing Detection Technique (IIA-SDT), exploiting the inherent consistency between TEC values derived from DF pseudo-range measurements and external references in spoofing-free scenarios. Spoofing probably disrupts this consistency: in simulator-based full-channel spoofing where all channels are spoofed, the inaccuracies of the offline ionospheric model used by the spoofer inevitably cause TEC mismatches; in partial-channel spoofing where the spoofer fails to control all channels, an unintended PVT deviation is induced, which also causes TEC deviations due to the spatial variation of the ionosphere. Basic principles and theoretical analysis of the proposed IIA-SDT are elaborated in the paper. Simulations using ionospheric data collected from 2023 to 2024 at a typical mid-latitude location are conducted to evaluate IIA-SDT performance under various parameter configurations. With a window length of 5 s and satellite number of 8, the annual average detection probability approximates 75% at a false alarm rate of $1\times {10}^{-3}$, with observable temporal variations. Field experiments across multiple scenarios further validate the spoofing detection capability of the proposed method. Full article

The Denoising Wavelet Transform Convolutional Long Short-Term Memory Network (DWTConvLSTM) is a novel ionospheric total electron content (TEC) spatiotemporal prediction model proposed in 2025 that can simultaneously consider high-frequency and low-frequency features while suppressing noise. However, it also has flaws as it only considers unidirectional temporal features in spatiotemporal prediction. To address this issue, this paper adopts a bidirectional structure and designs a bidirectional DWTConvLSTM model that can simultaneously extract bidirectional spatiotemporal features from TEC maps. Furthermore, we integrate a lightweight attention mechanism called Convolutional Additive Self-Attention (CASA) to enhance important features and attenuate unimportant ones. The final model was named CASA-BiDWTConvLSTM. We validated the effectiveness of each improvement through ablation experiments. Then, a comprehensive comparison was performed on the 11-year Global Ionospheric Maps (GIMs) dataset, involving the proposed CASA-BiDWTConvLSTM model and several other state-of-the-art models such as C1PG, ConvGRU, ConvLSTM, and PredRNN. In this experiment, the dataset was partitioned into 7 years for training, 2 years for validation, and the final 2 years for testing. The experimental results indicate that the RMSE of CASA-BiDWTConvLSTM is lower than those of C1PG, ConvGRU, ConvLSTM, and PredRNN. Specifically, the decreases in RMSE during high solar activity years are 24.84%, 16.57%, 13.50%, and 10.29%, respectively, while the decreases during low solar activity years are 26.11%, 16.83%, 11.68%, and 7.04%, respectively. In addition, this article also verified the effectiveness of CASA-BiDWTConvLSTM from spatial and temporal perspectives, as well as on four geomagnetic storms. Full article

A practical methodology for estimating regionally optimized Klobuchar coefficients using only International GNSS Service (IGS) Global Ionosphere Map (GIM) data is proposed. The method preserves computational simplicity, enabling near-real-time corrections suitable for accurate GNSS positioning. Utilizing both slant and vertical total electron content (STEC and VTEC) values extracted from GIM as inputs to estimate eight Klobuchar coefficients, robust parameter sets were obtained. Root mean square error (RMSE) analysis was used to compare these models to the standard Klobuchar model. Comprehensive performance evaluations using STEC-derived parameters, encompassing both seasonal and spatial analyses across South Korea, demonstrated significant reductions in ionospheric delay errors, with improvements reaching up to 57% compared to the conventional Klobuchar model. The far less computationally intensive VTEC-based model was applied over a wider region with 120 grid points. Continuous testing of this model over an entire year confirmed consistent enhancements in correction accuracy every day, demonstrating stable performance throughout the period. The developed regional Klobuchar models were further validated indirectly through satellite positioning performance, demonstrating daily RMSE improvements over the standard Klobuchar model ranging from 17.3% to 44.6%. Full article