The evaluation and prediction of ecological environmental quality are essential for sustainable development and environmental management, particularly in regions experiencing rapid urbanization and industrial growth like Johor in southern Peninsular Malaysia. This study evaluates the temporal and spatial changes in ecological environmental quality in Johor from 1990 to 2020 using the Remote Sensing Environmental Index (RSEI) and Cellular Automata-Markov (CA-Markov). A CA-Markov model was employed to predict ecological environmental quality for the next 12 months based on historical data. The results reveal significant changes over the 30 years, highlighting the dynamic nature of ecological conditions. The prediction results indicate that areas with excellent ecological quality are primarily focused in the central and northern regions, while the southern and eastern edges show mixed ecological conditions. The western region, characterized by intensive land use, shows significant environmental degradation. The poorest ecological points are mainly distributed in urban and semiurban areas with frequent human activities, such as cities, ports, and villages. These findings highlight the need for targeted environmental policies and management strategies to mitigate ecological degradation and promote sustainable development in Johor State of Peninsular Malaysia.

This paper presents an experimental and numerical analysis of the response of a scaled double-bottom structure with high and penetrated girders and floors impacted vertically by a rock-shaped indenter. The specimen, scaled from the bottom structure of the power-battery cabin of a new energy ship, is struck by a spherical indenter. The special double-bottom structure is designed to protect the power batteries and to facilitate heat dissipation. The experimental overall impact response, vibration acceleration, and stress of the inner bottom plate are measured in order to evaluate the impact environment in the target cabin. The investigation provides valuable information to evaluate the safety of power-battery cabins in a ship grounding scenario. The experimental results show good agreement with the finite element analyses using the explicit LS-DYNA software. The numerical analysis outlines the influence of the structural openings on the impact response and also the effect of battery mass and striking velocity on the impact environment in the target cabin.

This study presents an integrated experimental and numerical investigation into the collapse characteristics of a cracked box girder subjected to bidirectional cyclic bending moments. An experimental test involving a box girder specimen with a prefabricated transverse crack on the deck panel is conducted under four-point bending to evaluate the influence of cracking on ultimate strength under cyclic loading. The findings are reported through load–displacement curves, strain measurements, and observations of both global and localised structural failure modes, demonstrating strong consistency with finite element simulations conducted using ABAQUS software (version 2022). The results reveal that cyclic loading prior to ultimate capacity induces negligible stiffness reduction in the box girder structure, consistent with the structural behaviour under monotonic loading. The initial failure mechanism is attributed to local buckling of the deck plate, subsequently followed by significant plastic deformation around the crack tips, ultimately leading to global collapse. Parametric studies are carried out to evaluate the influence of key variables on the girder’s residual strength, such as crack length, cyclic load amplitude and pattern.

In the shipbuilding industry, the inefficiency of the successive approximation control method in CNC cold-bending machines has hindered productivity in steel bending manufacturing, particularly for rib profiles. This study proposes control methods for cold bending machines based on deep learning models to address this challenge, including CNN and Transformer-CNN (T-CNN), to predict the elastic spring-back rate of cold-processed metal profiles and generate precise control pulses for achieving target bending angles. Experimental validation using real-world datasets collected from a shipyard’s CNC cold bending machine demonstrates that the T-CNN model significantly reduces the number of steps required for each bending operation, achieving a 75% reduction in production time and substantially enhancing processing efficiency. By leveraging the strengths of CNNs and Transformer architectures, the T-CNN model excels at handling long sequence data and capturing global dataset characteristics. Results show that the T-CNN model outperforms traditional control methods and standard CNNs in prediction accuracy, stability, and efficiency, making it a superior choice for cold bending control.