Yellow–colored mesoporous pure titania and its high stability in visible light photocatalysis

AbstractYellow–colored pure titania with a mesoporous structure was prepared by the aggregate of titania nanocrystals, which were stabilized by exfoliated titanate nanosheets via an electrostatic interaction. X–ray diffraction patterns and images of transmission electron microscope confirm that titanate sheets are randomly dispersed into the assembled titania nanocrystals without forming any self–restacked phase. This nanocrystals–nanosheets composite exhibits a mesoporous structure with pore size of ~6.5nm and surface area of 236.3m2g−1. Greatly different from the UV–responded properties of titania nanocrystals and titanate nanosheets, the absorption edge of nanocomposite red–shifts to visible light region. The visible light photocatalytic tests demonstrate that this nanocomposited titania shows excellent activity for the degradation of organic dyes, as well as a colorless organic pollutant of 2, 4–dichlorophenol. The possible photocatalytic mechanism that photogenerated holes as the mainly oxidant species in photocatalysis is proposed based on the trapping experiments of hydroxyl radicals or photogenerated holes. Moreover, as the nanocomposite depicts an extreme stability, no obvious deactivation occurs after five cycles

Effects of Different Materials on the Tribological Performance of PVD TiN Films under Starved Lubrication Regime

Grit blasting is one simple but effective method to modify the morphology of material surface and can improve the tribological performance. In this study, a thick TiN film was prepared by arc ion plating on the steel disk treated with grit blasting, and the rough surface coated solid film was obtained. The tribological properties of solid film against different materials were evaluated under starved lubrication regime. The results showed that the friction coefficients of rough titanium nitride (TiN) films were lower than those of rough steel disks exclude alumina ball under starved lubrication, and the wear rates of TiN film were negligible due to the high hardness of TiN film and small contact area. For four kinds of balls including steel ball, silicon nitride, zirconia, and alumina, the wear scar diameter of steel ball is biggest, and the wear scar diameters of other balls are small. The hardness of steel ball is less than others, which results in the easy abrasion and increases the contact area to reduce the pressure. So the friction coefficient of TiN against steel is low and steady

Predicting Continuous Locomotion Modes via Multidimensional Feature Learning from sEMG

Walking-assistive devices require adaptive control methods to ensure smooth transitions between various modes of locomotion. For this purpose, detecting human locomotion modes (e.g., level walking or stair ascent) in advance is crucial for improving the intelligence and transparency of such robotic systems. This study proposes Deep-STF, a unified end-to-end deep learning model designed for integrated feature extraction in spatial, temporal, and frequency dimensions from surface electromyography (sEMG) signals. Our model enables accurate and robust continuous prediction of nine locomotion modes and 15 transitions at varying prediction time intervals, ranging from 100 to 500 ms. In addition, we introduced the concept of 'stable prediction time' as a distinct metric to quantify prediction efficiency. This term refers to the duration during which consistent and accurate predictions of mode transitions are made, measured from the time of the fifth correct prediction to the occurrence of the critical event leading to the task transition. This distinction between stable prediction time and prediction time is vital as it underscores our focus on the precision and reliability of mode transition predictions. Experimental results showcased Deep-STP's cutting-edge prediction performance across diverse locomotion modes and transitions, relying solely on sEMG data. When forecasting 100 ms ahead, Deep-STF surpassed CNN and other machine learning techniques, achieving an outstanding average prediction accuracy of 96.48%. Even with an extended 500 ms prediction horizon, accuracy only marginally decreased to 93.00%. The averaged stable prediction times for detecting next upcoming transitions spanned from 28.15 to 372.21 ms across the 100-500 ms time advances.Comment: 10 pages,7 figure

Serum Levels of FGF-21 Are Increased in Coronary Heart Disease Patients and Are Independently Associated with Adverse Lipid Profile

BACKGROUND: Fibroblast growth factor 21 (FGF-21) is a metabolic regulator with multiple beneficial effects on glucose homeostasis and lipid metabolism in animal models. The relationship between plasma levels of FGF-21 and coronary heart disease (CHD) in unknown. METHODOLOGY/PRINCIPAL FINDINGS: This study aimed to investigate the correlation of serum FGF-21 levels and lipid metabolism in the patients with coronary heart disease. We performed a logistic regression analysis of the relation between serum levels of FGF-21 and CHD patients with and without diabetes and hypertension. This study was conducted in the Departments of Endocrinology and Cardiovascular Diseases at two University Hospitals. Participants consisted of one hundred and thirty-five patients who have been diagnosed to have CHD and sixty-one control subjects. Serum FGF-21 level and levels of fasting blood glucose; triglyceride; apolipoprotein B100; HOMA-IR; insulin; total cholesterol; HDL-cholesterol; LDL-cholesterol; and C-reactive protein were measured. We found that median serum FGF-21 levels were significantly higher in CHD than that of control subjects (P<0.0001). Serum FGF-21 levels in CHD patients with diabetes, hypertension, or both were higher than that of patients without these comorbidities. Serum FGF-21 levels correlated positively with triglycerides, fasting blood glucose, apolipoprotein B100, insulin and HOMA-IR but negatively with HDL-C and apolipoprotein A1 after adjusting for BMI, diabetes and hypertension. Logistic regression analysis demonstrated that FGF-21 showed an independent association with triglyceride and apolipoprotein A1. CONCLUSIONS/SIGNIFICANCE: High levels of FGF-21 are associated with adverse lipid profiles in CHD patients. The paradoxical increase of serum FGF-21 in CHD patients may indicate a compensatory response or resistance to FGF-21

Circulating FGF21 Levels Are Progressively Increased from the Early to End Stages of Chronic Kidney Diseases and Are Associated with Renal Function in Chinese

Fibroblast growth factor 21 (FGF21) is a hepatic hormone involved in the regulation of lipid and carbohydrate metabolism. This study aims to test the hypothesis that elevated FGF21 concentrations are associated with the change of renal function and the presence of left ventricular hypertrophy (LVH) in the different stages of chronic kidney disease (CKD) progression.0.05).Plasma FGF21 levels are significantly increased with the development of early- to end-stage CKD and are independently associated with renal function and adverse lipid profiles in Chinese population. Understanding whether increased FGF21 is associated with myocardial hypertrophy in CKD requires further study

Accurate salient object detection via dense recurrent connections and residual-based hierarchical feature integration

Recently, the convolutional neural network (CNN) has achieved great progress in many computer vision tasks including object detection, image restoration, and scene understanding. In this paper, we propose a novel CNN-based saliency detection method through dense recurrent connections and residual-based hierarchical feature integration. Inspired by the recent neurobiological finding that abundant recurrent connections exist in the human visual system, we firstly propose a novel dense recurrent CNN module (D-RCNN) to learn informative saliency cues by incorporating dense recurrent connections into sub-layers of convolutional stages. Then we present a residual-based architecture with short connections for deep supervision which hierarchically combines both coarse-level and fine-level feature representations. Our end-to-end method takes raw RGB images as input and directly outputs saliency maps without relying on any time-consuming pre/post-processing techniques. Extensive qualitative and quantitative evaluation results on four widely tested benchmark datasets demonstrate that our method can achieve more accurate saliency detection results solutions with significantly fewer model parameters

A Novel Image Fusion Algorithm for Visible and PMMW Images based on Clustering and NSCT

Aiming at the fusion of visible and Passive Millimeter Wave (PMMW) images, a novel algorithm based on clustering and NSCT (Nonsubsampled Contourlet Transform) is proposed. It takes advantages of the particular ability of PMMW image in presenting metal target and uses the clustering algorithm for PMMW image to extract the potential target regions. In the process of fusion, NSCT is applied to both input images, and then the decomposition coefficients on different scale are combined using different rules. At last, the fusion image is obtained by taking the inverse NSCT of the fusion coefficients. Some methodologies are used to evaluate the fusion results. Experiments demonstrate the superiority of the proposed algorithm for metal target detection compared to wavelet transform and Laplace transform

Motion Characteristics of Collapse Body during the Process of Expanding a Rescue Channel

For the rapid construction of a rescue channel in the process of underground emergency rescue, a method for the expanded rescue channel in the collapse body is proposed and verified by a model test and a numerical simulation experiment. The motion characteristics and motion law of the expanded collapse body are analyzed on the basis of the mechanics of granular media, and a comparative simulation study on the main influencing factors of the collapse body motion is carried out. The results show that: (1) When the collapse body is expanded for a rescue channel, it will form three types of six relative slip planes. According to the position of the slip plane and the distribution of displacement, the collapse body can be divided into a direct displacement region, a stable region, and an indirect displacement region. (2) The expansion process can be divided into the initial start-up stage, the uplift stage, and the collapse stage, according to the formation time of the slip plane and the displacement law of the collapse body. (3) The results of the numerical simulation and the theoretical analysis of the granular media show that the dip angle of the slip plane is determined by the internal friction angle of the collapse particles, and the dip angles of the three slip planes are below &theta;1=90&deg;&minus;&phi;, &theta;2=45&deg;+&phi;/2, and &theta;3=90&deg;+&phi;/2. (4) The transverse scope and longitudinal distance is brought by the expansion increase with the increase in the expansion size, and the simulated dip angles of the slip plane are larger than the theoretical values due to the size effect. (5) In the expansion process, the strong force chain in the collapse body is concentrated in the stress arch above the expander device, and the failure and reconstruction laws of the stress arch at each stage are consistent with the formation of the slip plane and the uplift and instability law of the collapse body