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We present a detailed thermodynamic analysis of a sloshing spiral discovered in the intracluster medium (ICM) of the galaxy cluster Abell 2107, based on deep Chandra X-ray Observatory observations. Spectral analysis reveals that the spiral arm is significantly cooler and exhibits lower entropy and higher metallicity compared to the surrounding ICM, consistent with an origin in the cool, dense cluster core. We detect three cold fronts at the edges of the spiral, roughly aligned along an east-west axis. This alignment is consistent with predictions from numerical simulations of off-axis merger events. Subregion analysis of the spiral shows that the coldest and lowest-entropy gas is concentrated in the central part of the arm, while the side regions show evidence of mixing and interaction with the ambient medium. Among the examined thermodynamic quantities, entropy provides the most robust contrast between the sloshing gas and its surroundings. Notably, no optical or X-ray counterpart to a potential perturber is detected, suggesting that the responsible perturber may currently be below the detection limit or otherwise difficult to identify, while a dark matter-dominated or dark matter-only subhalo remains one possible scenario. Our findings provide new insights into the dynamical history of A2107 and demonstrate the importance of deep X-ray exposures for revealing subtle ICM structures in apparently relaxed clusters.

The behaviour of the pressure drag generated by trapped lee waves and upward-propagating internal waves in non-hydrostatic, stratified flow over a mountain ridge is investigated as a function of nonlinearity. A two-layer atmosphere is adopted, with piecewise-constant static stability and a uniform wind profile. The lower layer, between the surface and <mml:math><mml:mrow><mml:mi>z</mml:mi><mml:mo>=</mml:mo><mml:mi>H</mml:mi></mml:mrow></mml:math>, has stability <mml:math><mml:msub><mml:mrow><mml:mi>N</mml:mi></mml:mrow><mml:mrow><mml:mn>1</mml:mn></mml:mrow></mml:msub></mml:math>, and the upper layer extends indefinitely above with stability <mml:math><mml:msub><mml:mrow><mml:mi>N</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub></mml:math>, where <mml:math><mml:mrow><mml:msub><mml:mrow><mml:mi>N</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub><mml:mo>&lt;</mml:mo><mml:msub><mml:mrow><mml:mi>N</mml:mi></mml:mrow><mml:mrow><mml:mn>1</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:math>. Simulations are performed with a numerical model suitable for flows ranging from the microscale to the mesoscale, and nonlinearity is varied solely by increasing the mountain height. Linear reference values are obtained from the theoretical results reported by Teixeira et al., (2013). Two configurations are considered: (i) one in which trapped-lee-wave drag dominates over the drag due to propagating waves, and (ii) another in which the two components are of comparable magnitude. A set of diagnostics is introduced to clarify the physical processes associated with increasing nonlinearity. The results show that, as nonlinearity increases, the evolution of the total drag and its components is governed not only by the amplitude of the trapped lee waves, but primarily by the waveguide efficiency, which depends on the upstream Scorer parameter profile and the stable layer depth. This efficiency dictates whether the energy extracted from the incident flow through its interaction with the orography is largely retained and recycled within the trapped mode, or is instead transferred to propagating components and to processes associated with detuning and saturation. These findings may have implications for drag parametrisation in global climate and weather-prediction models.

The swift urbanization and administrative amalgamation among regions in Vietnam have intensified the demands on the construction area, requiring advanced vulnerability assessment tools that can capture inter-regional interactions and ecological integration. This research employed 10 m Sentinel-2 multispectral imagery alongside 317 field survey points to effectively classify land use land cover (LULC) through the application of Convolutional Neural Networks (CNN). The resultant LULC data was subsequently integrated with 16 terrain, natural, and anthropogenic variables within an Adaptive Neuro Fuzzy Inference System enhanced by an Artificial Bee Colony (ABC-ANFIS) framework, where the ABC algorithm optimized fuzzy membership functions and rule parameters to improve nonlinear predictions of vulnerability in urban development zones. The CNN model achieved remarkable classification performance, with Overall Accuracy (OA) exceeding 86% and a Kappa coefficient greater than 0.83, demonstrating its proficiency in distinguishing various cover types, especially constructed land, water features, and green areas. This investigation integrated both natural and anthropogenic terrain variables to create a vulnerability map of the construction area, attaining commendable results (R<SUP>2</SUP><SUB>adj</SUB> = 0.83; RMSE = 5.8; and MAE = 3.72). The findings indicated that Ho Chi Minh City, the Thu Duc -- Di An -- Thuan An urban corridor, and Ba Ria city emerged as resilience hotspots, with values surpassing 0.8, indicative of a high concentration of infrastructure coupled with a limited capacity for ecological buffering. Conversely, peri-urban ecological areas such as Can Gio, Phu Giao, and Xuyen Moc exhibited low vulnerability levels due to their extensive forest cover, water bodies, and agricultural landscapes which collectively contribute to environmental regulation. While the 10 m spatial resolution of Sentinel-2 satellite imagery is adequate for regional-scale analyses, it may constrain the detection of micro-scale infrastructure vulnerabilities in densely constructed urban environments. Overall, this research proposed a cohesive framework that integrates remote sensing and machine learning methodologies, aimed at enhancing resilience assessments in regions that have undergone administrative consolidation, thereby providing scientific guidance for spatial planning focused on sustainability and urban resilience.