Atmospheric and Oceanic Sciences

This research develops a predictive framework for cyber-induced navigational risk escalation in narrow canals and straits. Restricted manoeuvring space, dense traffic, and dependence on digital navigation systems amplify the operational consequences of cyber disruption in these environments. An expert-defined Bayesian Network (BN) was constructed to represent the causal relationships between ten risk node (RN) cyber-risk indicators, three intermediate degradation states, and the top event defined as cyber-induced navigational risk escalation in narrow waters. This top event does not represent attack initiation or attack occurrence directly; rather, it denotes a navigation-level risk state arising from the interaction of cyber-relevant vulnerabilities, compromised navigation information, and incorrect situation assessment. The BN benchmark was fitted using a Noisy-OR-type formulation. It was then evaluated alongside three machine-learning (ML) baselines: logistic regression, random forest, and gradient boosting, trained on the same RN predictors. The findings demonstrate the complementary strengths of both approaches, supporting the development of more reliable and explainable cyber-risk assessment tools for safety-critical maritime operations in narrow waterways. Because the TE labels were generated through BN-consistent simulation, the results should be interpreted as a controlled methodological proof-of-concept rather than as empirical validation of real-world maritime cyber-attack prediction accuracy.

The BepiColombo spacecraft, designed by ESA and JAXA, is currently in its cruise phase toward Mercury. Among the scientific investigations is the Mercury Orbiter Radio-science Experiment (MORE), which will exploit a multi-frequency microwave tracking system with an advanced Ka-band transponder to achieve its objectives pertaining to Mercury’s geodesy and fundamental physics. Leveraging precise measurements from this state-of-the-art radio tracking system, MORE is expected to provide new insights into Mercury’s interior, refining and expanding upon the findings of the MESSENGER mission. This work evaluates the performance of MORE’s gravity and rotation experiment, specifically assessing how BepiColombo’s improved radio tracking data can reduce uncertainties in the determination of Mercury’s gravity field, Love number k2, and rotational state. Differently from previous covariance analyses, this work includes errors in the dynamical model to assess the experiment’s performance under controlled mismodeling conditions. We present the results of a numerical simulation covering BepiColombo’s extended two-year orbital phase, with scientific operations set to begin in 2027.

Reliable battery state awareness is essential for energy management and power allocation in hybrid electric ships. However, battery management systems are increasingly exposed to False Data Injection Attacks (FDIAs) in intelligent connected environments, which can distort State of Charge (SOC) estimation and compromise the operational reliability of shipboard power systems. To address this challenge, this paper proposes a closed-loop “Modeling-Detection-Defense” framework for secure SOC estimation in marine cyber-physical energy systems. First, a stealthy FDIA model is developed based on battery dynamics and physical consistency constraints. Second, a hybrid detection method combining unsupervised and supervised learning is proposed to identify attacks. Finally, a long short-term memory network is employed to reconstruct compromised measurements and provide reliable SOC information for continuous energy management. Experimental results demonstrate that the proposed framework mitigates SOC estimation deviations caused by FDIAs. In addition, it effectively reduces power allocation errors and energy losses, thereby improving the cyber-resilience, operational reliability, and energy efficiency of hybrid ship power systems.

Maintenance dredging is a common practice in estuaries to ensure navigability and support port operations, but it also represents a widespread anthropogenic disturbance. Its effects on planktonic communities, however, remain poorly understood, particularly for mesozooplankton, a key component linking primary producers and higher trophic levels. This study evaluated the short-term responses of mesozooplankton communities and environmental variables to a maintenance dredging operation in the Guadalquivir estuary (SW Spain). Sampling was conducted at two sites with contrasting salinity conditions (Salinas: polyhaline and Puntalete: mesohaline) across three distinct phases: pre-, during, and post-dredging. Among the environmental variables analysed, pH showed a significant but transient decrease during dredging, while chlorophyll-a varied significantly among sampling phases. Turbidity tended to increase during dredging, although differences were not significant, and most other physicochemical variables remained largely unchanged. Overall, the observed environmental patterns were compatible with a short-term dredging signal but could not be confidently distinguished from the high natural temporal variability characteristic of the estuary. Biologically, the mesozooplankton community exhibited contrasting site-specific responses. In the polyhaline zone, species richness declined significantly and community composition shifted, with only partial recovery after dredging. Conversely, the mesohaline zone maintained a stable community composition but showed a marked decline in total abundance, representing the clearest biological response observed during dredging. These contrasting responses observed between sites suggest that the local ecological context may influence how estuarine ecosystems respond to short-term dredging-related disturbances.

Introduction Groundnut productivity under low-input production systems is often constrained by poor nutrient availability and unfavorable soil physical conditions. This study evaluated the interactive effects of polythene mulching and novel native microbial inoculants on crop growth, nodulation, rhizosphere microbial abundance, and productivity under zero-fertilizer conditions. Methods A two-year split-plot field study was conducted on new alluvial sandy loam soil to evaluate the interactive effects of polythene mulching and novel native microbial inoculants NRA1 (PP355674) and JCA-5 (PP809390) on rhizosphere microbial abundance and groundnut ( Arachis hypogaea L. cv. TG51) productivity under zero-fertilizer conditions. Growth, nodulation, rhizosphere microbial populations, yield attributes, and productivity were evaluated. Results Polythene mulching significantly enhanced vegetative growth, root biomass, nodulation, and pod yield, likely due to improved soil moisture conservation and microclimatic moderation. The Rhizobium–PSB consortium increased early nodulation (32.9 nodules plant - ¹) and rhizobial abundance (47.8 × 10 4 CFU g - ¹ soil), indicating improved symbiotic establishment. However, microbial inoculation did not significantly influence final yield despite enhancing nodulation and rhizosphere microbial abundance. At crop maturity, reduced total bacterial counts under mulching suggest possible effects of altered soil temperature, aeration, or substrate availability. Discussion The findings demonstrate that polythene mulching is the primary driver of crop productivity under zero-fertilizer conditions, whereas native microbial inoculants provide complementary benefits by enhancing nodulation and rhizosphere microbial functioning. Integrating polythene mulching with provenance-adapted microbial inoculants may improve resource-use efficiency and contribute to sustainable groundnut production in low-input alluvial agroecosystems.

The paper examines the morphodynamic development of reed-dominated phytogenic shores of the Dniprovsko–Buzky Liman, the largest river-mouth system in the northwestern Black Sea region. Based on field observations and analysis of Landsat and Sentinel satellite imagery acquired during 1985–2025, the spatial distribution of phytogenic shores, long-term dynamics of the external vegetation boundary, and moisture characteristics of the depositional substrate were investigated. The results revealed the predominance of progradational trends accompanied by pronounced spatial heterogeneity of morphodynamic processes. Variations in surface moisture were analyzed as an indirect indicator of substrate conditions potentially associated with long-term phytogenic shore development. The obtained results suggest that phytogenic shores should be considered complex biogeomorphological systems whose evolution is controlled by the interaction of hydrodynamic, lithodynamic and biotic factors.

In Unmanned Aerial Vehicle (UAV) object detection tasks, complex lighting conditions and variable weather render robust all-weather perception challenging when relying solely on the visible modality. Although infrared modalities can provide complementary information, the reliability of individual modalities is highly scene-dependent. Existing multimodal detection methods typically adopt static fusion strategies, which ignore spatial heterogeneity of modal reliability and under-explore spatial-frequency collaborative representation, thus limiting detection robustness in dynamic environments. To address these issues, this paper proposes a Dual-domain Enhanced Adaptive Fusion Network (DEAF-Net), with two core innovative modules to tackle the above challenges. First, the Dual Domain Progressive Refinement (DDPR) module mitigates feature degradation caused by poor imaging conditions via the joint design of frequency-domain learnable filtering and scale-aware contextual refinement in the spatial domain, effectively suppressing noise, enhancing textures, and yielding a purified feature basis for fusion. Second, the Consistency–Discrepancy Guided Fusion (CDGF) strategy leverages the selective scanning mechanism of VMamba to model consistent and differential patterns across modalities, dynamically generates local modal contribution maps for adaptive fusion, and integrates global scene prior via entropy weights for calibration. Extensive experiments on the DroneVehicle and VEDAI datasets show that DEAF-Net outperforms mainstream multimodal detection methods, achieving mAP@0.5 scores of 81.9% and 76.2%, respectively, while delivering improved robustness in low-light, dense fog, and sparse-category scenarios.

Detecting small targets in Unmanned Aerial Vehicle (UAV) imagery is challenging due to low pixel coverage, complex backgrounds, and information loss during downsampling. Existing detectors lack explicit mechanisms for enhancing weak target signals. We propose GHF-DETR, a Transformer-based detector featuring three collaboratively designed modules. First, a Heterogeneous Multi-Path Convolutional Network (HMC) backbone uses partial convolution and gated linear units to reduce computational redundancy while maintaining discrimination of small-object features. Second, a Dynamic Multi-Scale Focusing (DMSF) module integrates learned offset alignment with multi-kernel depthwise convolutions for cross-scale feature fusion. Third, a High-Frequency Selective Preservation (HSP) downsampling module combines space-to-depth convolution with 2D Discrete Wavelet Transform (DWT) to compensate for information loss in both spatial and frequency domains. On VisDrone2019, GHF-DETR achieves 33.1% mAP@0.5 and 18.6% mAP@0.5:0.95 with 15.4 GFLOPs and 7.59 M parameters, improving over the DFINE-n baseline by 5.4% and 3.1%, respectively, with AP_S reaching 10.1%. Generalization is validated on NWPU VHR-10. These results demonstrate that GHF-DETR achieves a favorable accuracy–efficiency balance for efficient UAV small-object detection.

Amebiasis cases in Japan are reported to the government according to the Infectious Diseases Control Law. Previous studies have shown significant reductions in total case numbers after 2018 and during the COVID-19 pandemic. This study aimed to clarify the recent trends of amebiasis cases in Japan, including during the pandemic period, with details on places of infection, using government surveillance data from January 1, 2001, to December 31, 2022. Change of time trends were modeled through piecewise mixed-effect regression model with knots set at 2018 and 2020. Year-to-year differences in case numbers were statistically assessed using Poisson regression model. And descriptive analyses of amebiasis cases reported in Japan by sex, age class, and prefecture (from 2001 to 2022) were conducted. Piecewise time trends of male-domestic cases showed increasing trend by 59.5 cases per year (p < 0.0001) before 2008. The trend slowed but still increased during 2008-2018, showing annual increase of 15.2 cases per year (p = 0.0014). A sharp decline occurred during 2018-2020, with cases decreasing by 219.0 cases per year (p < 0.0001). After 2020, the trend did not show statistically significant change (-16.9 cases per year, p = 0.6532). Poisson regression confirmed significant reductions in total and domestic cases between 2017-2018 and 2019-2020, while imported cases declined significantly only between 2019 and 2020. Male cases predominated, with most male cases in their 40s and 50s. Most cases of amebiasis have been reported in metropolitan areas. These results suggest that the decreased case numbers during the COVID-19 pandemic were due to not only the travel ban, but less socioeconomic activity. Furthermore, the epidemiology of amebiasis is similar to that of HIV infection in Japan, but the case numbers of amebiasis have not yet increased through 2022, showing a different trend from HIV infection and syphilis, the reason of which is unclear and needs further investigation.

The reliability of ecological niche models (ENMs) depends on the quality and representativeness of training data, which are often compromised by class imbalance in ecological datasets. This study evaluated how resampling strategies and training prevalence affect ENMs developed under class imbalance. Using freshwater benthic macroinvertebrate data from Korea, we compared non-resampled Control models with six resampling methods: random oversampling, synthetic minority oversampling technique (SMOTE), borderline SMOTE, adaptive synthetic sampling, density-based SMOTE, and random undersampling. These methods were tested across controlled prevalence scenarios and five ENM variants: maximum entropy (MaxEnt), random forest classifiers and regressors, and support vector machine classifiers and regressors. Resampling effects were strongest under severe-to-moderate class imbalance and diminished as training prevalence increased. Synthetic oversampling methods did not always generate the intended number of presence samples, and resampling-induced distributional shifts were largest at very low prevalence. Predictive gains were algorithm- and metric-dependent: MaxEnt showed little improvement, random forest models improved mainly in F1 and true skill statistic (TSS) with random undersampling, and support vector machine models benefited most consistently from SMOTE-based methods. Resampling also changed selected variables, variable-importance profiles, and accumulated local effect curves, particularly at low prevalence. These findings show that resampling is a context-dependent modeling decision that should be evaluated within the full ENM pipeline and selected according to the imbalance structure of the data and the predictive or ecological objectives of the study.

Accurate and robust localization is essential for intelligent vessels operating in complex marine and port environments. However, single-sensor localization is often affected by limited observation range, environmental occlusion, local interference, and sensor degradation. Although multi-sensor fusion can improve localization reliability, unknown cross-correlated measurement noise arising from shared disturbances, time synchronization errors, communication delays, and inconsistent fusion rates may degrade traditional information-filter-based fusion methods. To address this problem, this paper proposes an Adaptive Correlation Information Filter Network (ACIFNet) for multi-sensor fusion localization of intelligent vessels. ACIFNet preserves the recursive structure of the extended information filter and uses a Transformer-based network to learn adaptive information-domain fusion weights, thereby compensating for unknown inter-sensor correlations without explicitly estimating the full correlation covariance matrix. Experiments on constant-velocity, coordinated-turn (CV), and three-degree-of-freedom vessel motion models, together with a real-world restricted-waterway dataset, demonstrate that ACIFNet achieves higher localization accuracy and stability than Edge Incorporative Fusion (EIF)-inexact fusion, measurement fusion, and KalmanNet. In the CV and three-degree-of-freedom experiments, ACIFNet reduces the mean RMSE by 48.7%, 23.2%, and 26.1%, respectively, compared with KalmanNet. On the real-world dataset, ACIFNet achieves a mean position error of 9.90 m, an RMSE of 11.24 m, and a cross-track error of 8.72 m. These results show that ACIFNet effectively combines the interpretability of information filtering with the adaptive representation capability of neural networks for robust multi-sensor fusion localization under unknown cross-correlated measurement noises.

Cable Burial Risk assessment (CBRA) is undertaken to identify risks to offshore renewable energy cable infrastructure. CBRA assumes uniform soil conditions and does not recognise the effect of the cable installation methods. Centrifuge model testing was undertaken to explore the performance of a shipping anchor (AC-14) when encountering layered sand soil profiles. In addition, anchor interaction with both cable plough trenches and backfilled V-shaped pipeline plough trench routes were investigated. For a specific anchor, in loose over dense sand layers, penetration is stopped with minimal penetration into the underlying dense layer irrespective of the thickness of the loose layer for the anchor size investigated. Testing a recent layered soil CBRA approach indicated that it performed well when inputs were based upon well-characterised model anchor performance. Anchor interaction with vertical cable plough trenches showed limited modification of anchor behaviour. Similar observations were made for the V-shaped backfilled trenches where the anchor approached at 90 or 45° to the trench. When the anchor followed the route of the trench it dived through the trench and backfilled material and into the underlying dense soil. For CBRA methods to improve there is a need for high-quality characterisation of different anchor types in a wider range of soil conditions where realistic installation practices are considered.

Type 2 diabetes mellitus (T2DM) and sarcopenia demonstrate a significant comorbidity, particularly in the elderly, yet the molecular mechanisms linking them, especially through oxidative stress, remain incompletely understood. This study aimed to identify oxidative stress-related hub genes involved in T2DM-associated sarcopenia (T2DS) by integrating single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq data with machine learning. We analyzed scRNA-seq datasets (GSE244515, GSE268953) to characterize cellular heterogeneity and bulk RNA-seq datasets (GSE202295, GSE226151) for differential expression. Cell type annotation revealed key involvement of neuromuscular junctions and myofibers. Functional enrichment analyses highlighted pathways like the proteasome, TNF signaling, and ubiquitin-mediated proteolysis. From an initial set of oxidative stress-related genes, a comprehensive machine learning framework comprising 127 algorithm combinations was employed. The Lasso+Stepglm[both] model identified 12 candidate genes. Subsequent Protein-Protein Interaction (PPI) network analysis refined this to seven core hub genes: TNFRSF1B, PSMA2, UBE2D1, UBE2N, HSP90AA1, RAD23A, and DNAJB1. These genes are functionally interconnected, primarily implicating TNFRSF1B-mediated inflammatory signaling that activates the ubiquitin-proteasome system, leading to enhanced protein degradation-a key pathway in muscle atrophy. ROC curve analysis confirmed the strong diagnostic value of these hub genes across training, test, and external validation sets. Our findings systematically reveal novel oxidative stress-related hub genes and mechanisms in T2DS, providing potential biomarkers and therapeutic targets for this debilitating condition.