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Foamed concrete, due to its lightweight and high-strength characteristics, offers significant advantages as a roadbed material. However, its mechanical properties are notably influenced by its density and microstructure. Comprehensive research addressing the correlation between the density, mechanical properties, and microstructure of roadbed foamed concrete, especially within real construction scenarios, remains limited. To address this research gap, we undertook compressive strength tests and a set of microstructural analyses on four sets foamed concrete samples of varying densities prepared on-site. The objective was to deeply understand the mechanical and microstructural characterization of roadbed foamed concrete at different densities and correlation Analysis. The findings indicate an exponential correlation between density and compressive strength, most prominently within the density range of 800–1000 kg/m3, accompanied by commendable thermal stability. X-ray Computed Tomography (X-CT) analyses further demonstrate that density increase is pivotal in enhancing the pore structure, thereby achieving lower porosity, fewer detrimental pores, more homogeneous pore distribution, and superior pore sphericity. Scanning Electron Microscopy (SEM) evaluations suggest that enhancing the density of the sample can mitigate the damage to individual pores, reduce internal cavities, and curb initial defects. Furthermore, the pore intersections exhibit a more intact and dense crystalline structure. Upon assessment, we discerned a significant correlation between porosity, compressive strength, and density. The Pearson correlation coefficient (r) between any two of these parameters is notably close to 1 (−1). These research results show that it is feasible to optimize the density of foam concrete to enhance its microstructure and thereby enhance its compressive performance. This provides strong data support for the wider application of foam concrete in roadbed engineering.

Cognitive competency is an essential complement to the existing ship pilot screening system that should be focused on. Situation awareness (SA), as the cognitive foundation of unsafe behaviors, is susceptible to influencing piloting performance. To address this issue, this paper develops an identification model based on random forest- convolutional neural network (RF-CNN) method for detecting at-risk cognitive competency (i.e., low SA level) using wearable EEG signal acquisition technology. In the poor visibility scene, the pilots’ SA levels were correlated with EEG frequency metrics in frontal (F) and central (C) regions, including α/β (p = 0.071

The study of tumor microenvironment plays an important role in the treatment of cancer patients. In this paper, intelligent medical Internet of Things technology was used to analyze cancer tumor microenvironment-related genes. Through experiments designed and analyzed cancer-related genes, this study concluded that in cervical cancer, patients with high expression of P16 gene had a shorter life cycle and a survival rate of 35%. In addition, through investigation and interview, it was found that patients with positive expression of P16 and Twist genes had a higher recurrence rate than patients with negative expression of both genes; high expression of FDFT1, AKR1C1, and ALOX12 in colon cancer is associated with short survival; high expressions of HMGCR and CARS1 is associated with longer survival; overexpression of NDUFA12, FD6, VEZT, GDF3, PDE5A, GALNTL6, OPMR1, and AOAH in thyroid cancer is associated with shortened survival; high expressions of NR2C1, FN1, IPCEF1, and ELMO1 is associated with prolonged survival. Among the genes associated with the prognosis of liver cancer, the genes associated with shorter survival period are AGO2, DCPS, IFIT5, LARP1, NCBP2, NUDT10, and NUDT16; the genes associated with longevity are EIF4E3, EIF4G3, METTL1, NCBP1, NSUN2, NUDT11, NUDT4, and WDR4. Depending on the prognostic role of genes in different cancers, they can influence patients to achieve the effect of reducing patients’ symptoms. In the process of disease analysis of cancer patients, this paper uses bioinformation technology and Internet of things technology to promote the development of medical intelligence.

Respiratory monitoring is a significant issue to reduce patient risks and medical staff labor in postoperative care and epidemic infection, particularly after the COVID-19 pandemic. Oximetry is widely used for respiration monitoring in the clinic, but it sometimes fails to capture a low-functional respiratory condition even though a patient has breathing difficulty. Another approach is breathing-sound monitoring, but this is unstable due to the indirect measurement of lung volume. Kobayashi in our team is developing a sensor measuring temporal changes in lung volume with a displacement sensor attached across the sixth and eighth ribs. For processing these respiratory signals, we propose the combination of complex-valued wavelet transform and the correlation among spectrum sequences. We present the processing results and discuss its feasibility to detect a low-functional condition in respiration. The result for detecting low-functional respiration showed good performance with a sensitivity of 0.88 and specificity of 0.88 to 1 in its receiver operating characteristic (ROC) curve.