A Comparison of Chitosan Adhesion to KOH and H2O2 Pre-Treated Electrospun Poly(3-Hydroxybutyrate) Nanofibers

Chitosan coatings could effectively increase the biostability and biocompatibility of biomaterials while maintaining their structural integrity. In this study, electrospun fibrous polyhydroxybutyrate (PHB) membranes were pre-treated with potassium hydroxide (KOH) or hydrogen peroxide (H2O2) and then modified with dopamine (DA) and glutaraldehyde (GA) to improve their adhesion with chitosan (CS). Scanning electron microscopy (SEM), water contact angles (WCA), and Fourier transform infrared spectroscopy (FTIR) were used to demonstrate the successful generation of DA and GA-modified PHB fibers. KOH pre-treated PHB membranes exhibited superior binding efficiency with CS at low concentrations compared to their H2O2 pre-treated counterparts. The thermal analysis demonstrated a considerable decrease in the degradation temperature and crystallinity of KOH pre-treated membranes, with temperatures dropping from 309 °C to 265.5 °C and crystallinity reducing from 100% to 25.59% as CS concentration increased from 0 to 2 w/v%. In comparison, H2O2 pre-treated membranes experienced a mild reduction in degradation temperature, from 309 °C to 284.4 °C, and a large decrease in crystallinity from 100% to 43%. UV-vis analysis using Cibacron Brilliant Red 3B-A dye (CBR) indicated similar binding efficiencies at low CS concentrations for both pre-treatments, but decreased stability at higher concentrations for KOH pre-treated membranes. Mechanical testing revealed a considerable increase in Young’s modulus (2 to 14%), toughness (31 to 60%), and ultimate tensile stress (UTS) (14 to 63%) for KOH-treated membranes compared with H2O2 pre-treated membranes as CS concentration increased from 0 to 2 w/v%

The Effect of EMU Driver Operating Time on Professional Psychological Quality

Purpose: EMU driver operation time is an important part of the locomotive crew system. To ensure the safe, efficient and accurate operation of the EMU (Electric Multiple Unit), drivers not only need to have good physical and mental health but also must be able to work under the conditions of a scientific, reasonable and humanized organization.Design/methodology/approach: To effectively analyze the actual job of an EMU driver and to avoid impacting the normal work of the drivers, we selected some of the items from the professional mentality test project, which we had found had resulted in short test times and high test reliability and validity.Findings: With a single-driver continuous value multiplied by a time of less than two hours, there were no significant differences; multiplied by more than 4 hours, there was a significant difference in psychological quality; specifically, the multiplied career mental quality level decreased significantly. The EMU single continuous value multiplied by driving time driver should not be more than four hours to receive the full benefit. Originality/value: Based on the different operating times, this study compared the organization of different jobs in different situations. The negative impact of psychological load on EMU driver labor intensity varied

Electrospun PHB/Chitosan Composite Fibrous Membrane and Its Degradation Behaviours in Different pH Conditions

Peripheral nerve injury (PNI) is a neurological disorder that causes more than 9 million patients to suffer from dysfunction of moving and sensing. Using biodegradable polymers to fabricate an artificial nerve conduit that replicates the environment of the extracellular matrix and guides neuron regeneration through the damaged sites has been researched for decades and has led to promising but primarily pre-clinical outcomes. However, few peripheral nerve conduits (PNCs) have been constructed from controllable biodegradable polymeric materials that can maintain their structural integrity or completely degrade during and after nerve regeneration respectively. In this work, a novel PNC candidate material was developed via the electrospinning of polyhydroxy butyrate/chitosan (PHB/CS) composite polymers. An SEM characterisation revealed the resultant PHB/CS nanofibres with 0, 1 and 2 wt/v% CS had less and smaller beads than the nanofibres at 3 wt/v% CS. The water contact angle (WCA) measurement demonstrated that the wettability of PHB/CS electrospun fibres was significantly improved by additional CS. Furthermore, both the thermogravimetric analysis (TGA) and differentiation scanning calorimetry (DSC) results showed that PHB/CS polymers can be blended in a single phase with a trifluoracetic solvent in all compositions. Besides, the reduction in the degradation temperature (from 286.9 to 229.9 °C) and crystallinity (from 81.0% to 52.1%) with increasing contents of CS were further proven. Moreover, we found that the degradability of the PHB/CS nanofibres subjected to different pH values rated in the order of acidic > alkaline > phosphate buffer solution (PBS). Based on these findings, it can be concluded that PHB/CS electrospun fibres with variable blending ratios may be used for designing PNCs with controlled biodegradability

A Comparison of Chitosan Adhesion to KOH and H_{2}O_{2} Pre-Treated Electrospun Poly(3-Hydroxybutyrate) Nanofibers

Chitosan coatings could effectively increase the biostability and biocompatibility of biomaterials while maintaining their structural integrity. In this study, electrospun fibrous polyhydroxybutyrate (PHB) membranes were pre-treated with potassium hydroxide (KOH) or hydrogen peroxide (H2O2) and then modified with dopamine (DA) and glutaraldehyde (GA) to improve their adhesion with chitosan (CS). Scanning electron microscopy (SEM), water contact angles (WCA), and Fourier transform infrared spectroscopy (FTIR) were used to demonstrate the successful generation of DA and GA-modified PHB fibers. KOH pre-treated PHB membranes exhibited superior binding efficiency with CS at low concentrations compared to their H2O2 pre-treated counterparts. The thermal analysis demonstrated a considerable decrease in the degradation temperature and crystallinity of KOH pre-treated membranes, with temperatures dropping from 309 °C to 265.5 °C and crystallinity reducing from 100% to 25.59% as CS concentration increased from 0 to 2 w/v%. In comparison, H2O2 pre-treated membranes experienced a mild reduction in degradation temperature, from 309 °C to 284.4 °C, and a large decrease in crystallinity from 100% to 43%. UV-vis analysis using Cibacron Brilliant Red 3B-A dye (CBR) indicated similar binding efficiencies at low CS concentrations for both pre-treatments, but decreased stability at higher concentrations for KOH pre-treated membranes. Mechanical testing revealed a considerable increase in Young’s modulus (2 to 14%), toughness (31 to 60%), and ultimate tensile stress (UTS) (14 to 63%) for KOH-treated membranes compared with H2O2 pre-treated membranes as CS concentration increased from 0 to 2 w/v%

Research on Hydraulic Characteristics in Diversion Pipelines under a Load Rejection Process of a PSH Station

Transient analysis in diversion pipelines should be performed to ensure the safety of a hydropower system. After the establishment of a three-dimensional (3D) geometric model from the part upstream reservoir to the diversion pipeline end in a pumped storage hydropower (PSH) station, the hydraulic characteristics of the diversion system were solved by Reynold average Navier&ndash Stokes (RANS) equations based on a volume of fluid (VOF) method under the condition of simultaneous load rejection of two units. The variations of the water level in the surge tank, the pressure at the pipeline end, and the velocity on the different pipeline sections with time were obtained through the calculation. The numerical results showed that the water level changing in the surge tank simulated by VOF was consistent with the field test data. These results also showed that a self-excited spiral flow occurs in the pipeline when the flow at the end of the pipeline was reduced to zero and its intensity decreased with the flow energy exhaustion. The discovery of the self-excited spiral flow in the study may provide a new explanation for the pressure wave attenuation mechanism. Document type: Articl

A Hyper-pixel-wise Contrastive Learning Augmented Segmentation Network for Old Landslide Detection Using High-Resolution Remote Sensing Images and Digital Elevation Model Data

As a harzard disaster, landslide often brings tremendous losses to humanity, so it's necessary to achieve reliable detection of landslide. However, the problems of visual blur and small-sized dataset cause great challenges for old landslide detection task when using remote sensing data. To reliably extract semantic features, a hyper-pixel-wise contrastive learning augmented segmentation network (HPCL-Net) is proposed, which augments the local salient feature extraction from the boundaries of landslides through HPCL and fuses the heterogeneous infromation in the semantic space from High-Resolution Remote Sensing Images and Digital Elevation Model Data data. For full utilization of the precious samples, a global hyper-pixel-wise sample pair queues-based contrastive learning method, which includes the construction of global queues that store hyper-pixel-wise samples and the updating scheme of a momentum encoder, is developed, reliably enhancing the extraction ability of semantic features. The proposed HPCL-Net is evaluated on a Loess Plateau old landslide dataset and experiment results show that the model greatly improves the reliablity of old landslide detection compared to the previous old landslide segmentation model, where mIoU metric is increased from 0.620 to 0.651, Landslide IoU metric is increased from 0.334 to 0.394 and F1-score metric is increased from 0.501 to 0.565

Identifying the most influential roads based on traffic correlation networks

Prediction of traffic congestion is one of the core issues in the realization of smart traffic. Accurate prediction depends on understanding of interactions and correlations between different city locations. While many methods merely consider the spatio-temporal correlation between two locations, here we propose a new approach of capturing the correlation network in a city based on realtime traffic data. We use the weighted degree and the impact distance as the two major measures to identify the most influential locations. A road segment with larger weighted degree or larger impact distance suggests that its traffic flow can strongly influence neighboring road sections driven by the congestion propagation. Using these indices, we find that the statistical properties of the identified correlation network is stable in different time periods during a day, including morning rush hours, evening rush hours, and the afternoon normal time respectively. Our work provides a new framework for assessing interactions between different local traffic flows. The captured correlation network between different locations might facilitate future studies on predicting and controlling the traffic flows. © 2019, The Author(s)