Plant electrophysiology is undergoing a profound paradigm shift from traditional phenomenological observation to systemic signal decoding, with mature methodologies from computational neuroscience and brain-computer interface technologies providing critical theoretical and engineering support for th...

The growing urgency of climate change mitigation has increased pressure to develop reliable systems capable of monitoring and predicting carbon emissions. Conventional carbon accounting methods based on manual reporting and periodic environmental assessments frequently produce delayed and incomplete...

Artificial Intelligence of Things (AIoT) is transforming linear waste management into intelligent, data-driven solutions that support circular economy (CE) principles. This narrative review synthesizes peer-reviewed studies from 2020–2025 on AIoT applications across intelligent waste sorting, data a...

Salient object detection aims to identify and segment the most visually prominent objects in images. Despite significant advances in deep learning, existing methods struggle to balance global context modeling, boundary preservation, and multi-scale feature integration. To address these limitations,...

Aiming at the interrupt storm performance bottleneck existing in high-speed gyro attitude signal acquisition of aerospace vehicles, an embedded heterogeneous multi-core data acquisition system based on Xilinx ZYNQ-7000 SoC is proposed for inertial measurement unit (IMU) gyro signal sampling. On the...

Dengue is a major public health concern in tropical and subtropical regions worldwide. The World Health Organization (WHO) classifies dengue as an endemic disease in more than 125 countries, with approximately 75% of the affected population residing in the Asia-Pacific region, including the Philippi...

Digital twin (DT) technology has evolved beyond static physical mapping into a dynamic intelligence layer for smart manufacturing. We argue that three capabilities are now decisive for advancing DT production lines in complex industrial environments: data-driven quality correction through nonlinear...

Precise segmentation of colorectal polyps in colonoscopy images is essential for timely cancer diagnosis and prevention. Nevertheless, current segmentation methods contend with intrinsic variability in polyp appearance, differences in size, shape, and texture, while preserving computational efficien...

This paper proposes an enhanced U-Net-based segmentation framework for road crack detection that effectively addresses issues such as incomplete segmentation, detail loss, environmental complexity, and crack-pixel imbalance. The model integrates multiple functional modules to improve segmentation pe...

Plant factories, as agricultural production systems characterized by high yield, efficient resource utilization, and advanced mechanization, have attracted increasing global attention. Packaging finished vegetables is a critical pre-shipment operation in plant-factory production, and its automation...