The Simultaneous Bilateral Surgical Procedure for Bilateral Primary Lung Cancer.

Objective: At present, the incidence of synchronous multiple primary lung cancer (SMPLC) is increasing, and the treatment is still a challenge. This study aims to investigate the appropriate surgical procedure for treating bilateral primary lung cancer simultaneously. Methods: A retrospective analysis was conducted on clinical data from 32 patients who underwent simultaneous bilateral lung cancer surgery in our team. This data included patient characteristics, pulmonary function indicators, surgical procedures, operation duration, chest tube removal time, postoperative hospital stay, and postoperative complications. Results: Out of the 32 patients, 15 were male, and 17 were female, with an average age of 56.4 ± 8.8 years. The average maximum diameter of the main and minor tumors was 1.8 ± 1.0 cm and 1.0 ± .5 cm, respectively. All surgeries were performed thoracoscopically through intercostal approach. The procedure for the minor tumor was performed first, followed by the main tumor operation after turning over. One case was converted to thoracotomy during the main tumor operation because of bleeding. Postoperative complications occurred in one patient. No instances of respiratory insufficiency or failure were observed after the operation, and there were no perioperative deaths or readmissions within 90 days. Conclusion: Simultaneous bilateral thoracoscopic surgery is deemed a secure and feasible option for eligible patients with bilateral primary lung cancer, and it is advisable to commence the operation on the minor tumor first.

Design and application of coal gangue sorting system based on deep learning.

With the advancement of science and technology, coal-washing plants are transitioning to intelligent, information-based, and professional sorting systems. This shift accelerates the construction a modern economic system characterized by green and low-carbon development, thereby promoting the high-quality advancement of the coal industry. Traditional manual gangue picking and multi-axis robotic arm gangue selection currently suffer from low recognition accuracy, slow sorting efficiency, and high worker labor intensity. This paper proposes a deep learning-based, non-contact gangue recognition and pneumatic intelligent sorting system. The system constructs a dynamic database containing key feature information such as the target gangue's contour, quality, and center of mass. The system elucidates the relationships between ejection speed, mass, volume, angle of incidence, and the impact energy matching mechanism. Demonstration experiments using the system prototype for coal gangue sorting reveal that, compared to existing robotic arm sorting methods in coal washing plants, this system achieves a gangue identification accuracy exceeding 97%, a sorting rate above 91%, and a separation time of less than 3 s from identification to separation, thereby effectively enhancing raw coal purity.

A Highly Stretchable, Self-Healing Elastomer with Rate Sensing Capability Based on a Dynamic Dual Network.

Flexible sensing materials have attracted tremendous attention in recent years because of their potential applications in the fields of health monitoring, artificial intelligence, and so on. However, the preparation of rate sensing materials with self-healing performance is always a huge challenge. Herein, we first report the design and synthesis of a highly stretchable, recyclable, self-healing polysiloxane elastomer with rate sensing capability. The elastomer is composed of a dynamic dual network with boron/oxygen dative bonds and hydrogen bonds, which overcomes the structural instability of conventional solid-liquid materials. It exhibits certain adhesion, satisfactory mechanical robustness, and superior elongation at break (up to 1171%). After heating treatment at 80 °C for 2-4 h, the mechanical properties of damaged materials can be almost completely restored. Because of the "solid-liquid" property of the elastomer, it has irreplaceable functions which can sense different rates by resistance change after blending with multiwalled carbon nanotubes, principally in the range of 10 mm/min-150 mm/min. Especially, this rate sensing elastomer can be personalized by 3D printing at room temperature. This rate sensing strategy coupled with the introduction of dynamic dual-network structure is expected to help design advanced wearable devices for human rhythmic movement.