Showing papers by "Henan University of Technology published in 2020"

Global ecosystem thresholds driven by aridity

Abstract: Aridity, which is increasing worldwide because of climate change, affects the structure and functioning of dryland ecosystems. Whether aridification leads to gradual (versus abrupt) and systemic (versus specific) ecosystem changes is largely unknown. We investigated how 20 structural and functional ecosystem attributes respond to aridity in global drylands. Aridification led to systemic and abrupt changes in multiple ecosystem attributes. These changes occurred sequentially in three phases characterized by abrupt decays in plant productivity, soil fertility, and plant cover and richness at aridity values of 0.54, 0.7, and 0.8, respectively. More than 20% of the terrestrial surface will cross one or several of these thresholds by 2100, which calls for immediate actions to minimize the negative impacts of aridification on essential ecosystem services for the more than 2 billion people living in drylands.

An IGDT-based risk-involved optimal bidding strategy for hydrogen storage-based intelligent parking lot of electric vehicles

Abstract: In a near future, electric vehicles (EVs) will constitute considerable part of transportation systems due to their important aspects such as being environment friendly. To manage high number of EVs, developing hydrogen storage-based intelligent parking lots (IPLs) can help power system operators to overcome caused problems by high penetration of EVs. In this work, a new method is applied to get optimal management of IPLs in an uncertain environment and provide optimal bidding curves to take part in power market. The main purpose of this work is to get optimal bidding curves with considering power price uncertainty and optimal operation of IPLs. To model uncertainty of power price in the power market and develop optimal bidding curve, the opportunity, deterministic and robustness functions of the information gap decision theory (IGDT) technique has been developed. Obtained results has been presented in three strategies namely risk-taker, risk-neutral, and risk-averse corresponding to opportunity, deterministic, and robustness functions of the IGDT technique. In order to demonstrate the effects of demand response program (DRP), each strategy is optimized with and without DRP cases. The mixed-integer non-linear programming model is used to formulate the proposed problem which is solved using the GAMS optimization software under DICOPT solver.

Polysaccharide from Rosa roxburghii Tratt Fruit Attenuates Hyperglycemia and Hyperlipidemia and Regulates Colon Microbiota in Diabetic db/db Mice.

Abstract: This study was aimed at investigating the hypoglycemic and hypolipidemic effects of a polysaccharide (RTFP) isolated from Rosa roxburghii Tratt fruit on type-2 diabetic db/db mice. The results indicated that the oral administration of RTFP could significantly decrease the body weight, fat, and liver hypertrophy and the levels of fasting blood glucose, serum insulin, and serum lipids of the db/db mice. Histopathological observation showed that RTFP could effectively protect the pancreas, liver, and epididymal fat against damage and dysfunction. Real-time quantitative polymerase chain reaction analysis confirmed that the gene expression levels of peroxisome proliferator-activated receptors-γ (PPAR-γ), sterol regulatory element-binding protein-1 (SREBP-1c), acetyl-CoA carboxylase-1 (ACC-1), fatty acid synthase (FAS), and glucose-6-phosphatase (G6 Pase) were significantly down-regulated in the liver of db/db mice after treatment with RTFP. Moreover, RTFP treatment reversed gut dysbiosis by lowering the Firmicutes-to-Bacteroidetes ratio and enhancing the relative abundances of beneficial bacteria including Bacteroidaceae, Bacteroidaceae S24-7 group, and Lactobacillaceae. These findings suggest that RTFP can be used as a promising functional supplement for the prevention and treatment of type-2 diabetes mellitus.

Foodborne Titanium Dioxide Nanoparticles Induce Stronger Adverse Effects in Obese Mice than Non-Obese Mice: Gut Microbiota Dysbiosis, Colonic Inflammation, and Proteome Alterations.

Abstract: The recent ban of titanium dioxide (TiO2 ) as a food additive (E171) in France intensified the controversy on safety of foodborne-TiO2 nanoparticles (NPs). This study determines the biological effects of TiO2 NPs and TiO2 (E171) in obese and non-obese mice. Oral consumption (0.1 wt% in diet for 8 weeks) of TiO2 (E171, 112 nm) and TiO2 NPs (33 nm) does not cause severe toxicity in mice, but significantly alters composition of gut microbiota, for example, increased abundance of Firmicutes phylum and decreased abundance of Bacteroidetes phylum and Bifidobacterium and Lactobacillus genera, which are accompanied by decreased cecal levels of short-chain fatty acids. Both TiO2 (E171) and TiO2 NPs increase abundance of pro-inflammatory immune cells and cytokines in the colonic mucosa, indicating an inflammatory state. Importantly, TiO2 NPs cause stronger colonic inflammation than TiO2 (E171), and obese mice are more susceptible to the effects. A microbiota transplant study demonstrates that altered fecal microbiota by TiO2 NPs directly mediate inflammatory responses in the mouse colon. Furthermore, proteomic analysis shows that TiO2 NPs cause more alterations in multiple pathways in the liver and colon of obese mice than non-obese mice. This study provides important information on the health effects of foodborne inorganic nanoparticles.

All-Carbon Quaternary Stereocenters α to Azaarenes via Radical-Based Asymmetric Olefin Difunctionalization.

Abstract: A radical-based asymmetric olefin difunctionalization strategy for rapidly forging all-carbon quaternary stereocenters α to diverse azaarenes is reported. Under cooperative photoredox and chiral Bronsted acid catalysis, cyclopropylamines with α-branched 2-vinylazaarenes can undergo a sequential two-step radical process, furnishing various valuable chiral azaarene-substituted cyclopentanes. The use of the rigid and confined C2-symmetric imidodiphosphoric acid catalysts achieves high enantio- and diastereo-selectivities for these asymmetric [3 + 2] cycloadditions.

An improved genetic algorithm for the flexible job shop scheduling problem with multiple time constraints

Abstract: The flexible job shop scheduling problem is a very important problem in factory scheduling. Most of existing researches only consider the processing time of each operation, however, jobs often require transporting to another machine for the next operation while machines often require setup to process the next job. In addition, the times associated with these steps increase the complexity of this problem. In this paper, the flexible job scheduling problem is solved that incorporates not only processing time but setup time and transportation time as well. After presenting the problem, an improved genetic algorithm is proposed to solve the problem, with the aim of minimizing the makespan time, minimizing total setup time, and minimizing total transportation time. In the improved genetic algorithm, initial solutions are generated through three different methods to improve the quality and diversity of the initial population. Then, a crossover method with artificial pairing is adopted to preserve good solutions and improve poor solutions effectively. In addition, an adaptive weight mechanism is applied to alter mutation probability and search ranges dynamically for individuals in the population. By a series of experiments with standard datasets, we demonstrate the validity of our approach and its strong performance.

Vehicle-Damage-Detection Segmentation Algorithm Based on Improved Mask RCNN

Abstract: Traffic congestion due to vehicular accidents seriously affects normal travel, and accurate and effective mitigating measures and methods must be studied. To resolve traffic accident compensation problems quickly, a vehicle-damage-detection segmentation algorithm based on transfer learning and an improved mask regional convolutional neural network (Mask RCNN) is proposed in this paper. The experiment first collects car damage pictures for preprocessing and uses Labelme to make data set labels, which are divided into training sets and test sets. The residual network (ResNet) is optimized, and feature extraction is performed in combination with Feature Pyramid Network (FPN). Then, the proportion and threshold of the Anchor in the region proposal network (RPN) are adjusted. The spatial information of the feature map is preserved by bilinear interpolation in ROIAlign, and different weights are introduced in the loss function for different-scale targets. Finally, the results of self-made dedicated dataset training and testing show that the improved Mask RCNN has better Average Precision (AP) value, detection accuracy and masking accuracy, and improves the efficiency of solving traffic accident compensation problems.

Structural characterization and antioxidant activity of polysaccharides extracted from jujube using subcritical water

Abstract: Polysaccharides were extracted from jujube fruit using an efficient, optimized subcritical water extraction process. A maximum crude polysaccharide yield of 7.9% was obtained at an extraction temperature of 140 °C and a time of 60 min. The crude polysaccharides extracted under these conditions were purified. Three polysaccharide fractions, ZP1, ZP2 and ZP3, were obtained and their structures were investigated. The three fractions were mainly composed of arabinose, galactose, glucose, mannose, rhamnose, and galacturonic acid. ZP3 was further investigated using methylation and NMR spectroscopy, and ZP3 comprised a main chain of (1 → 4)-α-D-GalAp, (1 → 2,4)-α-L-Rhap, with a (1 → 4,6)-α-D-Galp branch. Assays of antioxidant activity showed that ZP3 exhibited a remarkable DPPH capacity and moderate hydroxyl radical scavenging ability, suggesting that it has potential applications as a natural antioxidant in foods and pharmaceuticals.

Ultrasmall Ru Nanoparticles Highly Dispersed on Sulfur-Doped Graphene for HER with High Electrocatalytic Performance.

Abstract: Nanostructuring and metal-support interactions have been explored as effective methods to improve the electrocatalytic activity in heterogeneous catalysis. In this study, we have fabricated ultrasmall Ru nanoparticles (NPs) dispersed on S-doped graphene (denoted as Ru/S-rGO) by a facile "one-pot" procedure. The experimental results indicated that both the S doping and moderate degree of oxidization of GO can induce the formation and high dispersion of the ultrasmall Ru NPs with larger electrochemically active surface areas for exposing more active sites. Metal-support interaction between S-doped graphene and Ru NPs was observed from the X-ray photoelectron spectroscopy and electronic charge-difference studies. It resulted in the decrease in the electron density of Ru, which facilitated electron release from H2O and H-OH bond breakage. The results of density functional theory calculation confirmed that the S-dopants could reduce the energy barrier for breaking the H-OH bond to accelerate water dissociation during the alkaline hydrogen evolution reaction (HER). At a current density 20 mA cm-2, the lowest overpotential of 14 mV, superior to that of Pt/C in alkaline solution, was observed for Ru/S-rGO-24. The observed lowest value of overpotential was because of the ultrasmall size, high dispersion, and metal-support interaction. This work provides a simple and effective method in designing advanced electrocatalysts for the HER in an alkaline electrolyte.

Event-Based Dissipative Analysis for Discrete Time-Delay Singular Jump Neural Networks

Abstract: This paper investigates the event-triggered dissipative filtering issue for discrete-time singular neural networks with time-varying delays and Markovian jump parameters. Via event-triggered communication technique, a singular jump neural network (SJNN) model of network-induced delays is first given, and sufficient criteria are then provided to guarantee that the resulting augmented SJNN is stochastically admissible and strictly stochastically dissipative (SASSD) with respect to $(\mathcal {X}_{\iota },\mathcal {Y}_{\iota },\mathcal {Z}_{\iota },\delta)$ by using slack matrix scheme. Furthermore, employing filter equivalent technique, codesigned filter gains, and event-triggered matrices are derived to make sure that the augmented SJNN model is SASSD with respect to $(\mathcal {X}_{\iota },\mathcal {Y}_{\iota },\mathcal {Z}_{\iota },\delta)$ . An example is also given to illustrate the effectiveness of the proposed method.

Supramolecular G4 Eutectogels of Guanosine with Solvent‐Induced Chiral Inversion and Excellent Electrochromic Activity

Abstract: Supramolecular eutectogels, an emerging class of materials that have just developed very recently, offer a new opportunity for generating functional supramolecular gel materials in biocompatible anhydrous or low-water media. As the first example of supramolecular G4 eutectogels, complexes of natural guanosine and H3 BO3 exhibited excellent gelation capacity in choline chloride/alcohol deep eutectic solvents. The as-prepared supramolecular eutectogels displayed unexpected solvent-induced chiral inversion and significantly high ionic conductivity (up to 7.78 mS cm-1 ), as well as outstanding thixotropic/injectable properties, high thermal stability and excellent electrochromic activity. These features make these versatile supramolecular G4 eutectogels promising candidates for developing next-generation flexible electronics with low environmental impact.

Cooperative photoredox and chiral hydrogen-bonding catalysis

Abstract: Chiral hydrogen-bonding catalysis is a classic strategy in asymmetric organocatalysis, and it has been extensively used in a variety of fundamental chemical transformations. At the same time, visible light-driven photoredox catalysis is a powerful and sustainable tool commonly used in radical chemistry. The intriguing combination of these two catalysis platforms would open a new avenue for the direct and highly efficient synthesis of enantioenriched compounds. Inspired by the conceptual breakthrough of T. Bach, in recent years, significant progress has been made in cooperative photoredox and chiral hydrogen-bonding catalysis. By developing a variety of important types of reactions, a wide range of valuable chiral compounds have been successfully synthesized. In this review, the advances in this key area are systematically described, and the examples are organized according to the distinct bond-forming patterns in the construction of the stereocentres.

In Situ Formation of Hierarchical Bismuth Nanodots/Graphene Nanoarchitectures for Ultrahigh-Rate and Durable Potassium-Ion Storage.

Abstract: Metallic bismuth (Bi) has been widely explored as remarkable anode material in alkali-ion batteries due to its high gravimetric/volumetric capacity. However, the huge volume expansion up to ≈406% from Bi to full potassiation phase K3 Bi, inducing the slow kinetics and poor cycling stability, hinders its implementation in potassium-ion batteries (PIBs). Here, facile strategy is developed to synthesize hierarchical bismuth nanodots/graphene (BiND/G) composites with ultrahigh-rate and durable potassium ion storage derived from an in situ spontaneous reduction of sodium bismuthate/graphene composites. The in situ formed ultrafine BiND (≈3 nm) confined in graphene layers can not only effectively accommodate the volume change during the alloying/dealloying process but can also provide high-speed channels for ionic transport to the highly active BiND. The BiND/G electrode provides a superior rate capability of 200 mA h g-1 at 10 A g-1 and an impressive reversible capacity of 213 mA h g-1 at 5 A g-1 after 500 cycles with almost no capacity decay. An operando synchrotron radiation-based X-ray diffraction reveals distinctively sharp multiphase transitions, suggesting its underlying operation mechanisms and superiority in potassium ion storage application.