IVIM-derived parameters differ depending on the calculation methods. The two-step fitting method with D value estimation first was correlated with DCE MRI perfusion.

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Being a driver in the control cabin of a shield tunneling machine is a high-pressure career. The physiological responses and mood swings of a driver are vital to his occupational health and construction safety; however, a driver's emotional intensity and physiological reactions in a noisy environment have not been considered. This study aims to investigate how a driver's emotional intensity and physiological responses in a noisy environment can be altered. On-site measurements were conducted in an urban metro system, and an emotional survey was performed. A wearable device was used as a physiological measurement tool to obtain heart rate data from a driver. Results indicate that the driver pays considerable attention to the noisy environment. The sound pressure level in the control cabin was in the range of 96.8 to 98.7 dBA. The driver's emotion is influenced when the sound pressure level increases to 94.5 dBA. The relationship was significant between the emotional intensity and the sound pressure level. The fear was highly evident with the sound pressure level increase given that the drivers were concerned about operational errors. The heart rate of the driver was significantly influenced in the noisy environment for a long time. The increased heart rate at 92 to 96 dBA was faster than at other ranges of sound pressure level. The emotional intensity had impacts on the heart rate of the driver. The disliking influenced the heart rate more obviously than the other two emotional types. The driver's emotion has a relationship with social background.

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Guided by the tolerance factor and average electronegativity difference, two stable garnets with compositions Ca3BTiGe3O12 (B = Mg, Zn) were designed, synthesized followed by structural, and dielectric characterization. The phase purity and structural characteristics were analyzed using X‐ray, Rietveld refinement, and microstructural analysis through scanning electron microscopy. A cubic structure with an Ia‐3d space group was confirmed for synthesized compositions. A combination of microwave dielectric properties for both garnets suggested that Ca3MgTiGe3O12 ceramic possessed a much higher quality factor (Q × f) ∼ 84 000 ± 3000 GHz coupled by a higher dielectric constant (εr) ∼ 12.97 ± 0.03, and a smaller temperature coefficient of resonance frequency (τf) ∼ −29.4 ± 1.5 ppm/°C compared to its Zn counterpart (Q × f ∼ 45 000 ± 2000 GHz, εr ∼ 12.84 ± 0.03, and τf ∼ −33.19 ± 1.6 ppm/°C). Such differences in dielectric performances were further explored utilizing packing fraction, ion polarizability, bond valence, Raman, and infrared spectrum to understand structure–property relationship.

Homogeneous Charge Compression Ignition (HCCI) combines the characteristics of gasoline engine and diesel engine with high thermal efficiency and low emissions. However, since there is no direct initiator of combustion, it is difficult to control the combustion timing in HCCI engines under complex working conditions. In this paper, Neural Network Predictive Control (NNPC) for combustion timing of the HCCI engine is designed and implemented. First, the black box model based on Elman neural network is designed and developed to estimate the combustion timing. The fuel equivalence ratio, intake valve closing timing, intake manifold temperature, intake manifold gas pressure, and engine speed are chosen as the system inputs. Then, a NNPC controller is designed to control combustion timing by controlling the intake valve closing timing. Simulation results show that the Elman neural network black box model is capable of estimating the HCCI engine combustion timing. In addition, regardless of whether the HCCI engine is in constant or complex condition, the designed NNPC controller is capable of keeping the combustion timing within the ideal range. In particular, under New European Driving Cycle (NEDC) working conditions, the maximum overshoot of the controller is 28.95% and the average error is 1.03 crank angle degree. It is concluded that the controller has good adaptability and robustness.

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