Lung cancer is a malignant tumor with high metastatic ability and bone is the most common site of distant metastasis of it. However, the independent risk factors for bone metastases of lung cancer remain largely to be elucidated. Here, we conducted a retrospective study to evaluate the correlation between clinical–pathological parameters, serum levels of neuron-specific enolase and CYFRA21-1, and bone metastases in lung cancer patients. The results revealed that patients with bone metastases were younger than those without metastases. Adenocarcinoma was the most frequent type of histopathology in patients with bone metastases. And the incidence of bone metastasis in patients with adenocarcinoma was significantly higher than those with other histopathological subtypes ( p < 0.001). Furthermore, the serum concentration of neuron-specific enolase was significantly higher in patients with bone lesions than those without bone metastases. Multivariate logistic regression analysis showed that patients’ age (odds ratio = 1.024, p < 0.001), concentrations of neuron-specific enolase (odds ratio = 1.212, p = 0.004), and histopathological types (odds ratio = 0.995, p = 0.001) were the independent risk factors for bone metastases in patients with lung cancer. Thus, physicians should pay attention to these factors in order to identify bone metastasis earlier while patient was primarily diagnosed as having lung cancer.

Accurately docking mobile robots to various workstations on the factory floor is a common and essential task. The existing docking methods face three major challenges: intricate deployment procedures, susceptibility to ambient lighting, and incapacity to recognize product information during the docking process. This paper devises a novel approach that combines the features of ArUco and Data Matrix to form a composite marker termed “DataMatrix-ArUco-Hybrid” (DAH). The DAH pattern serves as a fiducial marker capable of concurrently providing both target pose information and product information. Detection of the DAH pattern is conducted by a cost-effective fiducial marker reader system, called “FiMa-Reader”, which comprises an embedded processing unit and an infrared camera equipped with a 940 nm fill-light to overcome lighting issues. The FiMa-Reader system effectively detects the DAH pattern under both well-lit and dimly lit conditions. Additionally, the implementation of the FiMa-Reader system leads to significant improvements in positioning accuracy, including an 86.42% improvement on the x-axis, a 44.7% improvement on the y-axis, and an 84.21% improvement in angular orientation when compared to traditional navigation methods. The utilization of FiMa-Reader presents an economically viable system capable of guiding mobile robots’ positioning with high precision in various indoor lighting conditions.