The advancement of colorectal cancer emphasizes how important it is for colonoscopic imaging to accurately segment polyps. Learning-based techniques have made significant progress in the field of polyp medical image segmentation; however, recurring issues such as the identification of small object segments, poorly defined lesion boundaries, and complex backgrounds still exist. In order to overcome these constraints, we introduce CDBT-Unet, a brand-new framework that enhances segmentation performance by integrating two significant innovations. Initially, the transformer layer's convolutional prior speeds up convergence and extracts the fine-grained local texture that is essential for tiny flat polyps. By prioritizing horizontal-vertical background relationships through cross-shaped attention, it improves boundary delineation in complex backgrounds by reducing computation and accelerating convergence. The intricate background and edge blurring issue of polyp segmentation is well-considered in this point. Second, in order to improve accuracy, our dual-path encoder uses the MaxViT block to strategically balance global dependency modeling and local feature preservation. Combining multilevel feature fusion with coordinate space focus mechanisms and channel refinement improves edge response in multiscale fusion. The issue of boundary blurring is the main focus. Under the same experimental setup, our model outperforms the state-of-the-art ConDseg model by 3.72% and the baseline (TransUnet) by 7.32% in terms of Dice scores when tested on the Kvasir-SEG and CVC-ClinicDB datasets. Even in the presence of motion artifacts or low contrast, the framework demonstrates exceptional robustness in segmenting polyps of various sizes. Furthermore, the attention maps that were produced enhanced interpretability and gave physicians practical knowledge about how to make decisions when modeling.

Robotic Vision Sensor Technology forms the basis for perception in automatic systems, making it possible for machines to interpret and interact with their surrounding environments. This article provides a systematic overview of different vision sensor technology and their operating principles, ranging from photodetector arrays to radar. The paper then analyzes the practical applications of vision sensor technology in three demanding scenarios, and suggests the need for multi-sensor fusion for more accurate vision information and robust automation. In the following section, the paper reviews the present advantages and challenges of multi-sensor systems in addition to their future developments in order to give a consolidated overview of the state of robotic vision.

Coal-fired power plants are major carbon emitters, making CCS a crucial transitional technology for emission reduction. Among the three approaches—pre-combustion, oxy-fuel combustion, and post-combustion—capturing CO₂ after combustion is considered the most feasible option for current power plants, primarily because of its straightforward process and relatively low modification expenses. CCS enables the continued use of fossil fuels while supporting the shift to low-carbon energy. In this study, an optimized scheduling framework for power systems is developed, incorporating both carbon trading mechanisms and demand response programs, aiming to achieve both low-carbon operation and energy efficiency. By integrating carbon capture technologies, green certificate mechanisms, and dynamic carbon emission factors, an integrated framework for calculating carbon emission costs is established. Various types of demand response, including, Shiftable Load , Curtailable Load , and Replaceable Load , are modeled to reflect user-side flexibility. A carbon flow tracking mechanism is developed to support dynamic carbon accounting. Case studies demonstrate that integrating price-driven and substitution-oriented demand response helps to significantly flatten the system load profile, reduces system operating costs, and lowers carbon emissions, thereby improving the flexibility and economic performance of integrated energy systems. This study provides theoretical insights and practical references for advancing sustainable low-carbon energy systems.

In clinical practice, monitoring physiological indicators is essential for evaluating human health. Conventional medical devices can detect many of these signals, but their responses are often limited by insufficient selectivity and unstable output, which restricts their reliability. Electrochemical sensors have therefore attracted extensive interest in biomedical analysis as tools for assessing health status. By combining simple operation with high stability, sensitivity, and quantitative capability, electrochemical biosensors offer a promising platform for medical testing. This review provides an overview of recent developments in electrochemical biosensors for the detection of key biological targets, with a particular focus on viruses, glucose, and cancer cells. We summarize representative detection strategies and sensing materials, and highlight the main technical challenges encountered in practical applications. Finally, we discuss potential directions for the future development of electrochemical sensors in biomedical diagnostics.