The Effect of Plasma Triglyceride-Lowering Therapy on the Evolution of Organ Function in Early Hypertriglyceridemia-Induced Acute Pancreatitis Patients With Worrisome Features (PERFORM Study): Rationale and Design of a Multicenter, Prospective, Observational, Cohort Study

Background: Acute pancreatitis (AP) is a potentially life-threatening inflammatory disease with multiple etiologies. The prevalence of hypertriglyceridemia-induced acute pancreatitis (HTG-AP) has been increasing in recent years. It is reported that early triglyceride (TG) levels were associated with the severity of the disease, and TG- lowering therapies, including medical treatment and blood purification, may impact the clinical outcomes. However, there is no consensus regarding the optimal TG-lowering therapy, and clinical practice varies greatly among different centers. Our objective is to evaluate the TG-lowering effects of different therapies and their impact on clinical outcomes in HTG-AP patients with worrisome features. Methods: This is a multicenter, observational, prospective cohort study. A total of approximately 300 patients with HTG-AP with worrisome features are planned to be enrolled. The primary objective of the study is to evaluate the relationship between TG decline and the evolution of organ failure, and patients will be dichotomized depending on the rate of TG decline. The primary outcome is organ failure (OF) free days to 14 days after enrollment. Secondary outcomes include new-onset organ failure, new-onset multiple-organ failure (MOF), new-onset persistent organ failure (POF), new receipt of organ support, requirement of ICU admission, ICU free days to day 14, hospital free days to day 14, 60-day mortality, AP severity grade (Based on the Revised Atlanta Classification), and incidence of systemic and local complications. Generalized linear model (GLM), Fine and Gray competing risk regression, and propensity score matching will be used for statistical analysis. Discussion: Results of this study will reveal the current practice of TG-lowering therapy in HTG-AP and provide necessary data for future trials

Microfabricated Gas Sensors Based on Hydrothermally Grown 1-D ZnO Nanostructures

In this thesis, gas sensors based on on-chip hydrothermally grown 1-D zinc oxide (ZnO) nanostructures are presented, to improve the sensitivity, selectivity, and stability of the gas sensors. Metal-oxide-semiconductor (MOS) gas sensors are well-established tools for the monitoring of air quality indoors and outdoors. In recent years, the use of 1-D metal oxide nanostructures for sensing toxic gases, such as nitrogen dioxide, ammonia, and hydrogen, has gained significant attention. However, low-dimensional nanorod (NR) gas sensors can be enhanced further. Most works synthesize the NRs first and then transfer them onto electrodes to produce gas sensors, thereby resulting in large batch-to-batch difference. Therefore, in this thesis six studies on 1-D ZnO NR gas sensors were carried out. First, ultrathin secondary ZnO nanowires (NWs) were successfully grown on a silicon substrate. Second, an on-chip hydrothermally grown ZnO NR gas sensor was developed on a glass substrate. Its performance with regard to sensing nitrogen dioxide and three reductive gases, namely, ethanol, hydrogen, and ammonia, was tested. Third, three 1-D ZnO nanostructures, namely, ZnO NRs, dense ZnO NWs, and sparse ZnO NWs, were synthesized and tested toward nitrogen dioxide. Fourth, hydrothermally grown ZnO NRs, chemical vapor deposited ZnO NWs, and thermal deposited ZnO nanoparticles (NPs) were tested toward ethanol. Fifth, the effect of annealing on the sensitivity and stability of ZnO NR gas sensors was examined. Sixth, ZnO NRs were decorated with palladium oxide NPs and tested toward hydrogen at high temperature. The following conclusions can be drawn from the work in this thesis: 1) ZnO NWs can be obtained by using a precursor at low concentration, temperature of 90 °C, and long reaction time. 2) ZnO NR gas sensors have better selectivity to nitrogen dioxide compared with ethanol, ammonia, and hydrogen. 3) Sparse ZnO NWs are highly sensitive to nitrogen dioxide compared with dense ZnO NWs and ZnO NRs. 4) ZnO NPs have the highest sensitivity to ethanol compared with dense ZnO NWs and ZnO NRs. The sensitivity of the NPs is due to their small grain sizes and large surface areas. 5) ZnO NRs annealed at 600 °C have lower sensitivity toward nitrogen dioxide but higher long-term stability compared with those annealed at 400 °C. 6) When decorated with palladium oxide, both materials form alloy at a temperature higher than 350 °C and decrease the amount of ZnO, which is the sensing material toward hydrogen. Thus, controlling the amount of palladium oxide on ZnO NRs is necessary

Numerical Study on Mechanical Responses during Quench Protection in High-Temperature Superconducting Coils

In this paper, mechanical responses and electro-thermal characteristics of a rare earth barium copper oxide (REBCO) high-temperature superconducting (HTS) insulated pancake coil during the quenching process are investigated through finite element modeling (FEM). Firstly, a two-dimensional axisymmetric electro–magneto–thermal–mechanical FEM model with real dimensions is developed. Based on the FEM model, a systematic study on the effects of the time taken to trigger the system dump, background magnetic field, material properties of constituent layers, and coil size on quench behaviors of an HTS-insulated pancake coil is implemented. The variations in the temperature, current, and stress–strain in the REBCO pancake coil are studied. The results indicate that an increase in the time taken to trigger the system dump can increase the peak temperature of the hot spot but has no influence on the dissipation velocity. An apparent slope change of the radial strain rate is observed when the quench occurs regardless of the background field. During quench protection, the radial stress and strain reach their maximum values and then decrease as the temperature decreases. The axial background magnetic field has a significant influence on the radial stress. Measures to reduce peak stress and strain are also discussed, which indicates that increasing the thermal conductivity of the insulation layer, copper thickness, and inner coil radius can effectively reduce the radial stress and strain

Pd decoration of on-chip grown ZnO nanorods for ethanol detection

We have decorated on - chip grown ZnO nanorods (NRs) with Pd particles by sputtering for better ethanol detection . Size of the sputtered Pd particles determines response of the ZnO NRs to ethanol . ZnO - Pd 2 nm sample is about twice to three times more sensitive to ethanol compared with ZnO - Pd 4 nm and ZnO - Pd 8 nm samples depending on temperature and concentration of ethanol . ZnO - Pd 2 nm response s and recover s also very fast , e specially at 450 °C . Bigger Pd particles will worsen and even terminate sensing performance of the sensor to ethanol , as seen with 4 nm and 8 nm Pd

Study on Denoising Method of Photoionization Detector Based on Wavelet Packet Transform

Aiming at the task of noise suppression caused by the photoionization detector (PID) monitoring signal of volatile organic compounds (VOCs) due to local non-uniformity of the photocathode surface of PID in the ionization chamber, this paper proposes an analytical method of a PID signal with the adaptive weight of the small wave package decomposition node. The PID signal is transmitted to the upper machine software through the single-chip microcontroller. The appropriate wavelet packet decomposition level is determined according to the time frequency characteristics of the original signal of the PID, and the optimal wavelet packet base is selected through the polynomial fitting of the signal quality evaluation index. By comparing the quality of signals processed by the traditional wavelet packet denoising method and the denoising method presented in this paper, the superiority of the proposed method in the denoising signals of PID was verified. This method can eliminate the noise generated by local non-uniformity on the photocathode surface of the PID ionization chamber in a high humidity environment, which lays a foundation for the accurate monitoring of VOCs in a high humidity environment

Modeling and Analysis of Voltage Ripple-Controlled SIDO Buck Converter in Pseudo-Continuous Conduction Mode with Limited Cross-Regulation and Fast Load Transient Performance

Typical voltage mode-controlled DC–DC converters with single-inductor dual-output (SIDO) in pseudo-continuous conduction mode (PCCM) have slow load transient performance, and cross-regulation still exists between their two outputs. For the purposes of suppressing the cross-regulation and improving the load transient performance, a voltage ripple control technique for a PCCM SIDO buck converter is proposed in this study. A description of the PCCM SIDO buck converter and the voltage ripple control technique is provided in detail. In addition, the small-signal model was established, and the cross-regulation was further compared by Bode plots, as well as the load range and the power losses being analyzed. The cross-regulation and load transient performance of the voltage ripple-controlled PCCM SIDO buck converter was studied and compared with the conventional voltage mode-controlled. Using the proposed voltage ripple control for PCCM SIDO buck converters, we found no cross-regulation or rapid load transient behavior, which was validated by experimental results

A First-Principles Study on the Structural and Carrier Transport Properties of Inorganic Perovskite CsPbI3 under Pressure

Lead halide perovskite has attracted intensive attention for pressure and strain detection. Principally, pressure-induced changes in the structure and resistance of perovskite may bring great potential for developing high-performance piezoresistive pressure sensors. Herein, for the first time, we study the structural changes and the hot carrier cooling process of perovskite CsPbI3 under pressure based on density functional theory and time-dependent density functional theory. The calculation results show that the lattice constant of CsPbI3 linearly decreases and the time and path of the hot carrier cooling process change apparently under pressure. Meanwhile, the pressure will change the transition dipole moment, and the position of the k-point will not affect the optical properties of perovskite. Subsequently, the electrical conductivity enlarges as the pressure increases due to the change in charge density caused by pressure, which will be helpful for its potential application in the pressure sensors

Modeling and Analysis of Voltage Ripple-Controlled SIDO Buck Converter in Pseudo-Continuous Conduction Mode with Limited Cross-Regulation and Fast Load Transient Performance

Typical voltage mode-controlled DC–DC converters with single-inductor dual-output (SIDO) in pseudo-continuous conduction mode (PCCM) have slow load transient performance, and cross-regulation still exists between their two outputs. For the purposes of suppressing the cross-regulation and improving the load transient performance, a voltage ripple control technique for a PCCM SIDO buck converter is proposed in this study. A description of the PCCM SIDO buck converter and the voltage ripple control technique is provided in detail. In addition, the small-signal model was established, and the cross-regulation was further compared by Bode plots, as well as the load range and the power losses being analyzed. The cross-regulation and load transient performance of the voltage ripple-controlled PCCM SIDO buck converter was studied and compared with the conventional voltage mode-controlled. Using the proposed voltage ripple control for PCCM SIDO buck converters, we found no cross-regulation or rapid load transient behavior, which was validated by experimental results

A Novel Gas Recognition Algorithm for Gas Sensor Array Combining Savitzky–Golay Smooth and Image Conversion Route

In recent years, the application of Deep Neural Networks to gas recognition has been developing. The classification performance of the Deep Neural Network depends on the efficient representation of the input data samples. Therefore, a variety of filtering methods are firstly adopted to smooth filter the gas sensing response data, which can remove redundant information and greatly improve the performance of the classifier. Additionally, the optimization experiment of the Savitzky–Golay filtering algorithm is carried out. After that, we used the Gramian Angular Summation Field (GASF) method to encode the gas sensing response data into two-dimensional sensing images. In addition, data augmentation technology is used to reduce the impact of small sample numbers on the classifier and improve the robustness and generalization ability of the model. Then, combined with fine-tuning of the GoogLeNet neural network, which owns the ability to automatically learn the characteristics of deep samples, the classification of four gases has finally been realized: methane, ethanol, ethylene, and carbon monoxide. Through setting a variety of different comparison experiments, it is known that the Savitzky–Golay smooth filtering pretreatment method effectively improves the recognition accuracy of the classifier, and the gas recognition network adopted is superior to the fine-tuned ResNet50, Alex-Net, and ResNet34 networks in both accuracy and sample processing times. Finally, the highest recognition accuracy of the classification results of our proposed route is 99.9%, which is better than other similar work