https://ojs.sgsci.org/journals/iaet <p><strong><em>Innovations in Applied Engineering and Technology</em></strong> is an international, peer-reviewed, open-access journal dedicated to disseminating knowledge across all engineering disciplines. It covers a wide spectrum of engineering topics, including electronics, artificial intelligence applications, information systems, kinetic processes in materials, and strength of building materials. The journal provides a platform for sharing cutting-edge advancements, major research outputs, and key achievements in engineering R&amp;D. It also encourages submissions on breakthroughs and innovations with significant economic and social impact, aiming to elevate them to international standards and contribute as a transformative force, ultimately shaping a better future for humanity.</p> <p><strong>ISSN(Online): 3029-231X</strong></p> Global Science Publishing en-US Innovations in Applied Engineering and Technology 3029-231X https://ojs.sgsci.org/journals/iaet/article/view/624 <p>To address the challenges in disease detection tasks within the medical and healthcare domain, particularly those associated with biopharmaceutical applications, such as insufficient feature representation, limited capability for multi-scale lesion recognition, and severe interference from complex backgrounds, this paper proposes a detection model named MobileMamba-HC, which integrates frequency-domain adaptive convolution with a spatial-channel collaborative attention mechanism. The proposed method aims to improve the perception of fine-grained lesion regions while maintaining model efficiency, thereby enabling more accurate and robust disease detection in medical images. Built upon the MobileMamba architecture, the proposed model fully exploits its strengths in long-sequence modeling and global dependency capture to effectively model long-range spatial relationships in medical images. On this basis, a Frequency-domain Adaptive Dilated Convolution (FADC) module is introduced. By dynamically modulating feature responses in the frequency domain, this module achieves adaptive perception of components at different scales and frequencies, thereby enhancing the model’s ability to represent multi-scale lesion structures. Meanwhile, a Spatial and Channel Synergistic Attention (SCSA) mechanism is designed to jointly model critical regions and discriminative features from both the spatial and channel dimensions, suppressing redundant information and background noise interference and further improving the discriminability of feature representations. Through the synergistic effect of these three components, the proposed model significantly strengthens its capacity for modeling complex medical images while preserving its lightweight characteristics. In the experimental section, extensive evaluations are conducted on multiple public medical imaging datasets, and comparative experiments are performed against various mainstream deep learning detection models. The experimental results demonstrate that the proposed MobileMamba-HC achieves superior performance over the compared methods across multiple evaluation metrics. In particular, it exhibits stronger robustness in small lesion detection and low-contrast scenarios. Furthermore, ablation studies verify the effectiveness and complementarity of the FADC module and theSCSA mechanism in improving model performance, confirming the contribution of each proposed component to the overall enhancement. These findings validate that the proposed MobileMamba-HC model achieves a favorable balance between performance and efficiency in medical image disease detection tasks, providing an effective solution for intelligent computer-aided diagnosis in complex medical scenarios.</p> Yuhan Dai Copyright (c) 2026 Yuhan Dai 2026-05-13 2026-05-13 0002 0002 10.62836/iaet.v5i1.0002 https://ojs.sgsci.org/journals/iaet/article/view/629 <p>The global stock of operational urban rail hubs faces a performance challenge: designed under earlier demand assumptions, many now exhibit severe transfer bottlenecks, fragmented pedestrian connectivity, and poorly resolved station-city interfaces. Conventional responses—structural reconfiguration and large-scale reconstruction—are increasingly constrained by fiscal austerity and spatial fixity in dense built-up areas. This paper addresses the absence of a systematic framework for micro-scale optimization of existing hubs under such constraints. A Diagnosis–Optimization–Evaluation framework is proposed, operationalized through the Station-City Minimal Intervention Maturity Model (SC-MIMM), integrating three-dimensional spatial conflict detection, quantifiable intervention thresholds, demand-based facility allocation, and closed-loop implementation verification. The framework is tested through a case study of a constrained interchange hub in a large Asian metropolitan rail network. Results indicate that a 30% spatial compression of vertical circulation elements—reconfiguring a 4.3-m staircase into a combined stair-escalator system—yields an approximately 5.6-fold improvement in transfer capacity under simulation-based evaluation conditions at approximately one-fifth of reconstruction cost. Four generalizable threshold conditions are identified under which micro-intervention constitutes the optimal upgrading strategy. These findings challenge the assumption that significant hub performance improvements require major reconfiguration, and provide a transferable methodological foundation for incremental hub upgrading in dense urban contexts.</p> Wenjun Dai Copyright (c) 2026 Wenjun Dai 2026-05-13 2026-05-13 0001 0001 10.62836/iaet.v5i1.0001