Motor Interaction Control Based on Muscle Force Model and Depth Reinforcement Strategy.

The current motion interaction model has the problems of insufficient motion fidelity and lack of self-adaptation to complex environments. To address this problem, this study proposed to construct a human motion control model based on the muscle force model and stage particle swarm, and based on this, this study utilized the deep deterministic gradient strategy algorithm to construct a motion interaction control model based on the muscle force model and the deep reinforcement strategy. Empirical analysis of the human motion control model proposed in this study revealed that the joint trajectory correlation and muscle activity correlation of the model were higher than those of other comparative models, and its joint trajectory correlation was up to 0.90, and its muscle activity correlation was up to 0.84. In addition, this study validated the effectiveness of the motion interaction control model using the depth reinforcement strategy and found that in the mixed-obstacle environment, the model's desired results were obtained by training 1.1 × 103 times, and the walking distance was 423 m, which was better than other models. In summary, the proposed motor interaction control model using the muscle force model and deep reinforcement strategy has higher motion fidelity and can realize autonomous decision making and adaptive control in the face of complex environments. It can provide a theoretical reference for improving the effect of motion control and realizing intelligent motion interaction.

Identification effect of least square fitting method in archives management.

Archives management plays an important role in the current information age. Solving the problem of identifying and classifying archives is essential for promoting the development of archives management. The Least Squares Support Vector Machine (LS-SVM) is obtained by introducing the least squares fitting method into SVM, which is good at solving nonlinear classification. A new wavelet function is used to improve the classifier. At the same time, the cross-validation method is used to optimize the kernel parameters. Finally, the fuzzy theory and LS-SVM are combined to obtain Fuzzy Least Squares Support Vector Machines (FLS-SVM). This FLS-SVM classifier can use the distance between the data points and the classification hyperplane to classify the data in the non-separable region. The performance of FLS-SVM is verified by simulation experiments. The experimental results show that the classification accuracy of FLS-SVM classifier in archive data sets is 98.7%, and the loss rate is only 0.26%. When the wavelet function is used as the kernel function, the average accuracy of the classifier reaches 98.38%. Experiments show that the proposed method has good classification performance. It verifies the feasibility and effectiveness of the least squares fitting method in file management identification and classification.

Technical design of an innovative biomass/gasification-driven power plant with heat recovery hybrid system: CO2 emission comparison between the designed plant and fossil fuel-powered plants.

This study aims to introduce, conceptualize, and design a novel biomass/gasification-driven hybrid energy configuration. The proposed hybrid configuration has four subsystems: reformer solid oxide fuel cell (RSOFC), biomass/gasification, homogeneous charge compression ignition engine (HCCIE) plus waste heat recovery system (WHRS). RSOFC and HCCIE systems are embedded to generate electric energy. The syngas required for these two subsystems is captured from the biomass/gasification subsystem. In addition to generating electrical energy, fuel cell is responsible for providing combustible fuel to the HCCIE subsystem. The embedded engine in the system can improve the proposed configuration efficiency by increasing the rate of electrical energy production. In addition, the dissipated heat of fuel cell and engine subsystems is recovered by WHRS. The proposed energy configuration is evaluated and discussed from energetically, exergetically and exergoeconomic and environmental aspects to obtain a comprehensive feasibility study of the plant. The offered hybrid design has new component's structure and relationships that have not been reported in the publications. The analysis indicated that the proposed hybrid configuration is capable of generating approximately 1100 kW and 366.3 W of electric and thermal power, respectively, with the overall energetic and exergetic efficiencies of 69.4% and 52.1%. Exergoeconomic analysis results revealed that the specific fuel cost of the total proposed configuration was approximately 1.96 USD per GJ. In addition, compared to a coal and petroleum oil-based power generation plants, the proposed hybrid configuration can have approximately 2.75-fold and 97.7% lower CO2 emissions, sequentially. Besides, the proposed system can rival other similar biomass-driven designs.

Photocatalytic degradation of air pollutant by modified nano titanium oxide (TiO2)in a fluidized bed photoreactor: Optimizing and kinetic modeling.

Volatile organic compound (VOC) removal by photocatalytic oxidation (PCO) is the practical and economical process to reduce air pollutants. Many conditions, such as temperature, initial concentration of VOC, relative humidity, gas flow rate, and light intensity, affected this process. Therefore, finding the optimal operating conditions for the PCO process can increase the efficiency of the process and also operate the process more economically. Also, it is possible to scale up the process with more confidence by the kinetics modeling of the process and finding the rate constants. In this study, the effect of gas flow rate, light intensity, and VOC inlet concentration were investigated. The results show that the flow rate of 15 lit/min is more efficient, and the effect of the pollutant input concentration and light intensity directly affects the conversion percentage. The kinetic study of acetaldehyde removal was investigated in the fluidized bed reactor, and the best kinetic model was proposed based on reactor model regression on the outlet concentration data. The best model describes a langmuir-hynshelwood type model with adsorbed acetaldehyde's inhibition effect on the catalyst's surface. The R2 coefficient for the best kinetic type is 0.98.

Design and Screening of New Lead Compounds for Autism Based on QSAR Model and Molecular Docking Studies.

The purpose of the present study aims to develop a satisfactory model for predicting pro-social and pro-cognitive effects on azinesulfonamides of cyclic amine derivatives as potential antipsychotics. The three dimensional-quantitative structure affinity relationship (3D-QSAR) study was performed on a series of azinesulfonamides of cyclic amine derivative using comparative molecular similarity indices analysis (CoMSIA). The best statistical model of CoMSIA q2, r2, SEE and F values are 0.664, 0.973, 0.087, and 82.344, respectively. Based on the model contour maps and the highest activity structure of the 43rd compound, serial new structures were designed and the 43k1 compound was selected as the best structure. The dock results showed a good binding of 43k1 with the protein (PDB ID: 6A93). The QSAR model analysis of the contour maps can help us to provide guidelines for finding novel potential antipsychotics.

Two-Dimensional Material-Based Electrochemical Sensors/Biosensors for Food Safety and Biomolecular Detection.

Two-dimensional materials (2DMs) exhibited great potential for applications in materials science, energy storage, environmental science, biomedicine, sensors/biosensors, and others due to their unique physical, chemical, and biological properties. In this review, we present recent advances in the fabrication of 2DM-based electrochemical sensors and biosensors for applications in food safety and biomolecular detection that are related to human health. For this aim, firstly, we introduced the bottom-up and top-down synthesis methods of various 2DMs, such as graphene, transition metal oxides, transition metal dichalcogenides, MXenes, and several other graphene-like materials, and then we demonstrated the structure and surface chemistry of these 2DMs, which play a crucial role in the functionalization of 2DMs and subsequent composition with other nanoscale building blocks such as nanoparticles, biomolecules, and polymers. Then, the 2DM-based electrochemical sensors/biosensors for the detection of nitrite, heavy metal ions, antibiotics, and pesticides in foods and drinks are introduced. Meanwhile, the 2DM-based sensors for the determination and monitoring of key small molecules that are related to diseases and human health are presented and commented on. We believe that this review will be helpful for promoting 2DMs to construct novel electronic sensors and nanodevices for food safety and health monitoring.

The Combination of Two-Dimensional Nanomaterials with Metal Oxide Nanoparticles for Gas Sensors: A Review.

Metal oxide nanoparticles have been widely utilized for the fabrication of functional gas sensors to determine various flammable, explosive, toxic, and harmful gases due to their advantages of low cost, fast response, and high sensitivity. However, metal oxide-based gas sensors reveal the shortcomings of high operating temperature, high power requirement, and low selectivity, which limited their rapid development in the fabrication of high-performance gas sensors. The combination of metal oxides with two-dimensional (2D) nanomaterials to construct a heterostructure can hybridize the advantages of each other and overcome their respective shortcomings, thereby improving the sensing performance of the fabricated gas sensors. In this review, we present recent advances in the fabrication of metal oxide-, 2D nanomaterials-, as well as 2D material/metal oxide composite-based gas sensors with highly sensitive and selective functions. To achieve this aim, we firstly introduce the working principles of various gas sensors, and then discuss the factors that could affect the sensitivity of gas sensors. After that, a lot of cases on the fabrication of gas sensors by using metal oxides, 2D materials, and 2D material/metal oxide composites are demonstrated. Finally, we summarize the current development and discuss potential research directions in this promising topic. We believe in this work is helpful for the readers in multidiscipline research fields like materials science, nanotechnology, chemical engineering, environmental science, and other related aspects.

In-situ synthesis of polypyrrole/silver for fabricating alginate fabrics with high conductivity, UV resistance and hydrophobicity.

In this research, the polypyrrole/silver (PPy/Ag) composite was first in-situ prepared on alginate fabrics by chemical oxidative polymerization of pyrrole (Py) monomer using silver nitrate as oxidant and sodium dodecyl sulfate (SDS) as the dopant. The effects of mole ratio of Py to silver nitrate, reaction time and dopant concentration on the preparation of PPy/Ag composite were optimized. It was found the optimal molar ratio of Py to silver nitrate was 1:1.5 with 0.02 M SDS under the reaction time of 10 h. Then, the microstructure and properties of resultant PPy/Ag composite were analyzed by scanning electron microscope (SEM), Fourier infrared spectrometer (FT-IR), X-ray diffraction spectroscopy (XRD), X-ray photoelectron spectroscopy (XPS), Raman spectroscopy and the thermogravimetry analysis (TGA), respectively. Finally, the influences of PPy/Ag coating on the performance of alginate fabrics including electrical conductivity, hydrophilicity, antistatic property and anti-ultraviolet capability were determined. It was found that the electrical conductivity of alginate fabric could be intensively increased after PPy/Ag coating. Meantime, the anti-ultraviolet capability and hydrophobicity could be largely improved by PPy/Ag coating especially under high Py dosage. This paper introduced a simple method for preparing PPy/Ag composite direct on alginate fabric to make it a good functional substrate which could be applied in many fields.

Low-voltage and fast-response SnO2nanotubes/perovskite heterostructure photodetector.

One-dimensional metal-oxides (1D-MO) nanostructure has been regarded as one of the most promising candidates for high-performance photodetectors due to their outstanding electronic properties, low-cost and environmental stability. However, the current bottlenecks are high energy consumption and relatively low sensitivity. Here, Schottky junctions between nanotubes (NTs) and FTO were fabricated by electrospinning SnO2NTs on FTO glass substrate, and the bias voltage of SnO2NTs photodetectors was as low as ∼1.76 V, which can effectively reduce energy consumption. Additionally, for improving the response and recovery speed of SnO2NTs photodetectors, the NTs were covered with organic/inorganic hybrid perovskite. SnO2NTs/perovskite heterostructure photodetectors exhibit fast response/recovery speed (∼0.075/0.04 s), and a wide optical response range (∼220-800 nm). At the same time, the bias voltage of heterostructure photodetectors was further reduced to 0.42 V. The outstanding performance is mainly attributed to the formation of type-II heterojunctions between SnO2NTs and perovskite, which can facilitate the separation of photogenerated carriers, as well as Schottky junction between SnO2NTs and FTO, which reduce the bias voltage. All the results indicate that the rational design of 1D-MO/perovskite heterostructure is a facile and efficient way to achieve high-performance photodetectors.

ITGA8 positive cells in the conventional outflow tissue exhibit Schlemm's canal endothelial cell properties.

AIMS: Elevated intraocular pressure is primarily induced by the increased resistance of conventional outflow of aqueous humor. Dysfunction of the juxtacanalicular region of trabecular meshwork (TM) and Schlemm's canal (SC) endothelium, as the main conventional outflow tissue, have been implicated as the major reasons for the increased resistance. Integrins are widespread in these tissues, especially alpha8 integrin (ITGA8). We aim to investigate the properties of cells expressing ITGA8 in the conventional outflow tissue. MAIN METHODS: Fluorescence in situ hybridization (FISH) and immunofluorescence (IF) were performed to detect the mRNA and protein levels of ITGA8 in human conventional outflow tissue. ITGA8-positive cells were isolated from the cultured human TM cells through a magnetic bead-based approach. Flow Cytometry was used to determine the purification efficiency. The expressions of TM and SC biomarkers and dexamethasone-induced myocilin secretion capacity of ITGA8-positive cells was assessed by Real-time PCR, IF and Western blot. A gel contraction assay was performed to evaluate contractility of ITGA8-positive cells after endothelin 1 treatment. KEY FINDINGS: ITGA8 was found with robust expression near the inner wall of SC endothelium. After purification, the proportion of ITGA8-positive cells were increased by about 10%. ITGA8-positive cells were identified with the properties as SC endothelial cells, such as more robust expressions of SC biomarkers, less dexamethasone-inducible myocilin expression, and stronger contractility. SIGNIFICANCE: This study demonstrated that cells expressing ITGA8 in SC region possess more properties as SC endothelial cells. Our data implicate a crucial role of ITGA8 in aqueous humor (AH) outflow resistance regulation.

Preparation of Ion-Exchanged TEMPO-Oxidized Celluloses as Flame Retardant Products.

Cellulose, as one of the most abundant natural biopolymers, has been widely used in textile industry. However, owing to its drawbacks of flammability and ignitability, the large-scale commercial application of neat cellulose is limited. This study investigated some TEMPO-oxidized cellulose (TOC) which was prepared by selective TEMPO-mediated oxidation and ion exchange. The prepared TOC was characterized by Fourier transform infrared (FT-IR) spectroscopy and solid-state 13C-nuclear magnetic resonance (13C-NMR) spectroscopy. The thermal stability and combustion performance of TOC were investigated by thermogravimetry analysis (TG), microscale combustion calorimetry (MCC) and limiting oxygen index (LOI). The results demonstrated that the thermal stability of TOC was less than that of the pristine material cellulose, but the peak of heat release rate (pHHR) and the total heat release (THR) of all TOC were significantly reduced. Additionally, the LOI values of all TOC products were much higher 25%. In summary, the above results indicated that the modified cellulose with carboxyl groups and metal ions by selective oxidation and ion exchange endows efficient flame retardancy.