Comparative study of two rolling bond process for super-thick Q235B

In paper, two rolling bond processes for heavy-gauge steel plate Q235B were studied and the processes were simulated by MARC software. The mechanical properties and microstructure at the interface were comparative analyzed for the two bonded plates using different rolling process. Using MARC software analysis for two rolling process, the ratio of equivalent stress in rolling process /yield stress in current temperature from surface to center portion was relatively uniform for rolling bonded

IEEE Access Special Section Editorial: Advanced Information Sensing and Learning Technologies for Data-Centric Smart Health Applications

Smart health is bringing vast and promising possibilities on the road to comprehensive health management. Smart health applications are strongly data-centric and, thus, empowered by two key factors: information sensing and information learning. In a smart health system, it is crucial to effectively sense individuals’ health information and intelligently learn from its high-level health insights. These two factors are also closely coupled. For example, to enhance the signal quality, a sensing array requires advanced information learning techniques to fuse the information, and to enrich medical insights in mobile health monitoring, we need to combine “multimodal signal processing and machine learning techniques” and “nonintrusive multimodality sensing methods.” In new smart health application exploration, challenges arise in both information sensing and learning, especially their areas of interaction

IEEE Access Special Section Editorial: Smart Health Sensing and Computational Intelligence: From Big Data to Big Impacts

Smart health big data is paving a promising way for ubiquitous health management, leveraging exciting advances in biomedical engineering technologies, such as convenient bio-sensing, health monitoring, in-home monitoring, biomedical signal processing, data mining, health trend tracking, and evidence-based medical decision support. To build and utilize the smart health big data, advanced data sensing and data mining technologies are closely coupled key enabling factors. In smart health big data innovations, challenges arise in how to informatively and robustly build the big data with advanced sensing technologies, and how to automatically and effectively decode patterns from the big data with intelligent computational methods. More specifically, advanced sensing techniques should be able to capture more modalities that can reflect rich physiological and behavioral states of humans, and enhance the signal robustness in daily wearable applications. In addition, intelligent computational techniques are required to unveil patterns deeply hidden in the data and nonlinearly convert the patterns to high-level medical insights

Machine Learning Electrocardiogram for Mobile Cardiac Pattern Extraction

Background: Internet-of-things technologies are reshaping healthcare applications. We take a special interest in long-term, out-of-clinic, electrocardiogram (ECG)-based heart health management and propose a machine learning framework to extract crucial patterns from noisy mobile ECG signals. Methods: A three-stage hybrid machine learning framework is proposed for estimating heart-disease-related ECG QRS duration. First, raw heartbeats are recognized from the mobile ECG using a support vector machine (SVM). Then, the QRS boundaries are located using a novel pattern recognition approach, multiview dynamic time warping (MV-DTW). To enhance robustness with motion artifacts in the signal, the MV-DTW path distance is also used to quantize heartbeat-specific distortion conditions. Finally, a regression model is trained to transform the mobile ECG QRS duration into the commonly used standard chest ECG QRS durations. Results: With the proposed framework, the performance of ECG QRS duration estimation is very encouraging, and the correlation coefficient, mean error/standard deviation, mean absolute error, and root mean absolute error are 91.2%, 0.4 ± 2.6, 1.7, and 2.6 ms, respectively, compared with the traditional chest ECG-based measurements. Conclusions: Promising experimental results are demonstrated to indicate the effectiveness of the framework. This study will greatly advance machine-learning-enabled ECG data mining towards smart medical decision support

Local strain analysis of the tertiary oxide scale formed on a hot-rolled steel strip via EBSD

This work presents a fine microstructure and local misorientation study of various oxide phases in the tertiary oxide scale formed on a hot-rolled steel strip via electron back-scattering diffraction (EBSD). Local strain in individual grains of four phases, ferrite (α-Fe), wustite (FeO), magnetite (Fe3O4) and hematite (α-Fe2O3), has been systematically analysed. The results reveal that Fe3O4 has a lower local strain than α-Fe2O3, in particular, on the surface and inner layers of the oxide scale. The multiphase oxides along the cracking or α-Fe2O3 penetration generally develop a high local misorientation. Localised stain along the cracks demonstrates that the misorientation tends to be strong near grain boundaries. The high fraction of small Fe3O4 grains accumulate at the oxide-substrate interface, which leads to a dramatic increase in the intensity of local stain. This variation is due mainly to the phase transformation among the oxide phases, i.e., the Fe3O4 particles during their nucleation and growth. The combined action of stress relief and re-oxidisation is proposed to explain the formation of Fe3O4 seam at the oxide-steel interface. The present study offers an intriguing insight into the deformation behaviour of the tertiary oxide scale formed on steels, and may help with understanding the stress-aided oxidation effect of metal alloys

Effects of grain boundaries in oxide scale on tribological properties of nanoparticles lubrication

The characters of grain boundaries in oxide layers formed on substrates influence adhesion and friction behaviour, surface fracture and wear during high temperature steel processing. In this work, an electron backscattered diffraction (EBSD) analysis was conducted to investigate the role of surface grain boundary and orientation in magnetite (Fe3O4)/haematite (α-Fe2O3) scale during hot rolling, and further evaluate their effects on tribological properties of water-based nanoparticles lubrication. The results demonstrate that Fe3O4 (100) plane is strongly sensitive to the surface characteristics as the minimisation of surface energy. Coincident site lattice (CSL) boundaries in microstructure is in presence of Σ3 in the Fe3O4 and Σ13b in the Fe2O3 of the substrates subjected to a thickness reduction of 28% and cooling rate of 28 ° C/s. This is due in great part to the changes in crystal slip systems. These low-Σ CSL boundaries in oxide scale offer obstacles to the propagation of cracks, where some of nanoparticles collected would be trapped at the interface and thereby may cause high wear rates. A lubrication mechanism is proposed to explain the grain boundary effect on nanoparticles lubrication, and further to determine the dependence of frictional behaviour on surface energy, crystallographic preferred orientation (microtexture) and crystal structure. These results provide an intriguing new insight into the application of water-based lubricant with graphite nanoparticles

Tribological analysis of oxide scales during cooling process of rolled microalloyed steel

The composition and phase transformation of oxide scale in cooling process (after hot rolling) of rolled microalloyed steels affect tribological features of rolled strip and downstream process, and the produced steel surface quality. In this study, physical simulation of surface roughness transfer during cooling process with consideration of ultra fast cooling (UFC) was carried out in Hille 100 experimental rolling mill, the obtained oxide scale was examined with SEM to show its surface and phase features. The results indicate that the surface roughness of the oxide scale increases as the final cooling (coiling) temperature increases, and the flow rate of the introduced air decreases. The cracking of the surface oxide scale can be improved when the cooling rate is 20 °C/s, the strip reduction is less than 12%, and the thickness of oxide scale is less than 15 μm, independent of the surface roughness. A cooling rate of more than 70 °C/s can increase the formation of retained wustite and primary magnetite precipitates other than the precipitation of α-iron. This study is helpful in optimising the cooling process after hot rolling of microalloyed steels to obtain quality surface products

Screening of Concentration and Antimicrobial Effectiveness of Antimicrobial Preservative in Betastatin Besylate Nasal Spray

Objective. To explore the optimal concentration and antimicrobial effectiveness of antimicrobial preservative in betastatin besylate nasal spray. Methods. By using Staphylococcus aureus, Pseudomonas aeruginosa, Escherichia coli, Candida albicans, and Aspergillus niger as test strains, the antimicrobial effectiveness of betastatin besylate nasal spray containing different concentrations of antimicrobial preservative (0.02%, 0.0125%, and 0.005% benzalkonium chloride, respectively) was determined by using bacteriostatic effect test (Chinese Pharmacopoeia, 2015 edition). Results. The antimicrobial effectiveness of betastatin besylate nasal spray containing 0.02% and 0.0125% benzalkonium chloride, respectively, complied with the regulations of Chinese Pharmacopoeia (2015 Edition) against five test strains. However, the antimicrobial effectiveness of betastatin besylate nasal spray containing 0.005% benzalkonium chloride against P. aeruginosa did not meet the requirements of Chinese Pharmacopoeia. Conclusion. Benzalkonium chloride at a concentration of 0.125% can be used as an added antimicrobial preservative in betastatin besylate nasal spray