Atmospheric and Oceanic Sciences

ABSTRACT Sediment transport pathways are the critical input for sediment-related coastal zone management programs. In the present study, seasonal variations in the granulometric characteristics, such as mean size and sorting of beach sands and corresponding changes in shore face morphology in response to near-shore wave climate and wave power distribution, are used to infer sediment transport pathways and depositional environment. For this study, the coastal stretch between Bhatkal on the north and Kundapura (Coondapur) on the south along the Central West Coast of India is considered. Significant wavebreaker heights, breaking wave group celerity and wave power were determined using Mike 21 software. Significant breaker height of the waves varied between 0.5 and 2.4m. The highest breaker energy of 7.25x103 Nm/m2 and wave power of 39.85x103 N/m2 was observed during the monsoon, while during the fairweather season, it was 0.45x103and 2.56x103N/m2respectively. The foreshore in the study area ranged from reflective to dissipative. The spatio-temporal cross-shore profiles and volumetric change analysis of the foreshore indicated dominant onshore- offshore sediment movement in the reflective beaches, and alongshore sediment movement in dissipative/transition beaches. Distinct variations in textural characteristics across different segments of the study area and foreshore morphology reflect the role of wave climate in driving the coastal dynamics.

The rapid growth in vessel size associated with global maritime trade is placing increasing pressure on port infrastructure worldwide. In Brazil, many existing ports face structural limitations due to insufficient navigational depth and limited opportunities for spatial expansion, often constrained by urban encroachment. In this context, identifying suitable coastal locations for deep-draft port development has become a key strategic challenge for long-term planning. This study develops a GIS-based spatial suitability model to identify segments of the Brazilian coastline with favourable conditions for deep-draft port infrastructure capable of accommodating large vessels, including post-Panamax ships. The approach considers physical constraints, environmental restrictions and basic logistical connectivity within a multi-criteria spatial framework implemented through map algebra. The model is conceived as a strategic screening tool to support early-stage decision-making rather than a detailed feasibility assessment. The results identify nine coastal locations with the highest suitability scores, indicating that highly favourable conditions for deep-draft port development are spatially limited. Notably, one of these candidate locations partially overlaps with an existing port-related cluster, suggesting consistency between the model outputs and real-world port development patterns. In contrast, large portions of the southeastern coastline (particularly in São Paulo and Paraná) exhibit lower suitability due to a combination of urban pressure, environmental constraints and limited depth conditions. Overall, the findings reveal a spatial mismatch between Brazil’s main economic core and the coastal areas with more favourable natural conditions for new port infrastructure. The proposed framework contributes a transparent and transferable spatial decision-support tool that can assist policymakers in identifying priority areas for future port development and in balancing investments between the expansion of existing ports and the development of new locations.

Introduction Marine macroalgae are increasingly recognized as promising sources of bioactive compounds for sustainable plant disease management. This study evaluated the anti-phytopathogenic potential of organic extracts from three intertidal macroalgal species, Ulva fasciata , Gracilaria khanjanapajiae , and Sargassum wightii , collected from Thalpe Reef, Sri Lanka. Methods Chloroform, ethyl acetate, and methanol extracts were screened against phytopathogenic fungi Aspergillus niger , Colletotrichum lindemuthianum , Colletotrichum fructicola , Sclerotinia sclerotiorum , and Fusarium oxysporum using the poisoned plate method, and against phytopathogenic bacteria Xanthomonas cucurbitae , Pectobacterium carotovorum , Pseudomonas syringae , Dickeya chrysanthemi , and Ralstonia sp. using the well diffusion assay. Bioassay-guided fractionation was performed on selected active extracts, and the resulting fractions were analyzed by gas chromatography–mass spectrometry. Results Antimicrobial activity varied significantly among macroalgal species, extraction solvents, concentrations, and target pathogens (ANOVA, p < 0.05). The ethyl acetate extract of G. khanjanapajiae exhibited the highest antifungal activity with 71.52 ± 1.60% inhibition against C. lindemuthianum . In contrast, the chloroform extract of U. fasciata showed the strongest antibacterial activity, producing a 24.2 ± 0.3 mm inhibition zone against D. chrysanthemi . Bioassay-guided fractionation further enhanced antimicrobial efficacy, with selected fractions demonstrating higher inhibition than crude extracts. GC–MS analysis of the most active fractions revealed diverse metabolites, including lipophilic phenolics, fatty acids, fatty amides, terpenes and terpenoid derivatives, long-chain alcohols, aldehydes, ketones, and long-chain hydrocarbons such as alkanes, alkenes, and α-olefins, many of which are known for antimicrobial properties. Discussion The findings necessitate further investigation of Sri Lankan marine macroalgae species and solvent-dependent variation of their antimicrobial potential, as candidates for environmentally compatible plant disease control strategies.

Carybdea prototypus has potent venom and is responsible for serious envenomation incidents in the coastal regions of Japan. However, the natural habitat of the benthic polyp, which is a source of medusae, has not yet been identified. Previous studies on cubozoans, including polyp collection records and environmental DNA analyses, have suggested that C. prototypus polyps may inhabit low-salinity environments. However, the role of salinity in regulating the life-history processes of this species remains unclear. Therefore, in this study, we examined the effects of salinity on key developmental processes in C. prototypus , including planula settlement, polyp growth, asexual reproduction, and medusae metamorphosis. The planulae and polyps were reared under controlled salinity conditions ranging from 15 to 33. Planulae were successfully attached across all salinity treatments, although survival was reduced at 15. Metamorphosis into creeping polyps proceeds faster under moderately low-salinity conditions. After attachment, the time required to transform into settled polyps decreased with decreasing salinity. Polyp proliferation was active at low salinity, whereas polyp size increased with increasing salinity. In contrast, metamorphosis into medusae occurred only under high-salinity conditions. At salinity of 15, polyps degenerate into tissue masses. These findings indicate a clear salinity-dependent shift in life history strategy, in which low salinity promotes clonal expansion through asexual reproduction. Meanwhile, higher salinity facilitates somatic growth and metamorphosis into medusae. This study provides experimental evidence that salinity regulates key life-history transitions in C. prototypus and suggests that this polyp may inhabit low-salinity environments such as estuaries and seagrass beds of Zostera marina . Fluctuations in salinity likely are instrumental in shaping the distribution and population dynamics of this species in coastal environments.

Under 10 min sparse Automatic Identification System (AIS) sampling, the reliability of point-wise motion statistics degrades substantially, and conventional classification methods rely on trajectory interpolation, which may introduce spurious motion patterns. This study proposes a feature-driven framework for fishing vessel type identification that eliminates the need for interpolation preprocessing. A 39-dimensional feature set is constructed using robust statistics, including the median and interquartile range, to characterize trajectory-level behavioral patterns. Adaptive speed interval thresholds are derived through a data-driven approach grounded in Bayesian decision boundaries, thereby removing the dependence on manually defined cut-off values. A backward ablation procedure guided by feature importance ranking identifies a lightweight 12-dimensional feature subset that retains 98.7% of the classification accuracy at a compression rate of 69%. Evaluated on 18,320 fishing vessel trajectories in the East China Sea, the full 39-dimensional feature set achieves a 5-fold cross-validation accuracy of 91.92% (Macro-F1 = 0.919, Kappa = 0.879), with inter-fold standard deviations ranging from 0.002 to 0.004. Comparative experiments demonstrate that three tree-based classifiers all exceed 90% accuracy on the same feature set, confirming that feature robustness, rather than model selection, constitutes the dominant performance factor. LightGBM achieves the optimal trade-off between accuracy and training efficiency, whereas the cross-validation standard deviation of LSTM is approximately 7.5 times greater, indicating that hand-crafted robust features provide superior stability under sparse sampling conditions. The proposed framework requires no fishery-specific prior knowledge and offers a transferable paradigm for sparse AIS trajectory analysis.

Abstract The essay describes the development of hydrogeology education in German universities over the past 25 years, based on the author’s own experience and an informal poll among German-speaking hydrogeology lecturers. Hydrogeology is an integral part of geoscience degree programmes in Germany, where it is increasingly recognized as an indispensable player in interdisciplinary efforts to solve societal, environmental and health-related problems. This is reflected by faculty positions in Germany shifting towards a focus on (bio) geochemistry and away from groundwater quantity-focused research.

Agricultural mechanization (AM) provides a new path for the organic connection between small farmers and modern agriculture. High-standard farmland construction (HSFC) enhances comprehensive agricultural production capacity by improving the quality of farmland elements and promoting AM. Based on panel data from farmers’ tracking surveys conducted in China from 2020 to 2022, this paper analyzes the impact of HSFC on AM using a panel data measurement model. The findings show that: (1) HSFC significantly improves AM. (2) HSFC can improve AM by expanding the agricultural operation scale (AOS) and reducing agricultural operation costs (AOC). (3) Compared with low-altitude agricultural machinery (LAM), high-standard farmland is more conducive to the application of traditional agricultural machinery (TAM). (4) There is a non-equilibrium effect on the influence of HSFC on AM. The influence of HSFC on AM will be affected by the development of the village economy. (5) Compared with low-income, hilly, and traditional villages, HSFC can improve AM of high-income, flat, and modern villages. This paper provides important empirical evidence for HSFC and AM. It provides a theoretical basis for improving the comprehensive agricultural production capacity by supporting HSFC and improving AM.

In unmanned farms, machinery transfer between fields and access to field entrances are essential prerequisites for autonomous field operations, and both require support from an accurately structured farm-road network. However, existing road data are typically maintained as vector layers and lack the topological relationships and geometric attributes needed for transfer-route and field-entrance planning. This study proposes a method for constructing farm-road topological maps from road and field vector data. The method converts road polygons into a node–edge graph containing centerline geometry, estimates road-segment widths to support the safe passage of agricultural machinery, and establishes bidirectional road–field associations based on field-access nodes. Experiments in three farm areas show that the proposed method achieves a mean symmetric centerline error of 0.094 m; width-estimation mean absolute error (MAE) and root mean square error (RMSE) of 0.032 m and 0.060 m, respectively; and a 100% success rate in 50 random path-planning tasks. The farm-road topological map constructed by this method provides spatial infrastructure for agricultural-machinery path planning and operation scheduling in unmanned farms.