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Dopamine homeostasis is an important clinical diagnostic index, because an abnormal level in the human body is closely related to certain serious diseases. Herein, a novel electrochemical sensing platform based on gold nanobipyramid/multi-walled carbon nanotube hybrids (AuNBP/MWCNTs) is developed to detect dopamine in human fluids. Using field emission scanning electron microscopy, it is observed that AuNBPs of about 60 nm with two pyramids are well dispersed on the surface of MWCNTs. Energy-dispersive X-ray spectrometry, X-ray diffraction and X-ray photoelectron spectroscopy confirm that AuNBPs are self-assembled onto the surface of MWCNTs to form the hybrids. Cyclic voltammetry reveals that the AuNBP/MWCNTs exhibit good electrocatalytic activity toward dopamine oxidation owing to the synergistic effects of AuNBPs and MWCNTs. In addition, both cyclic voltammetry and differential pulse voltammetry display three well-resolved and distinct oxidation peaks on the AuNBP/MWCNT-modified glassy carbon electrode. Based on AuNBP/MWCNTs, the newly developed electrochemical sensor is used to detect dopamine in the presence of ascorbic acid and uric acid over a wide linear range from 50 nM to 2.7 mM and a low detection limit of 15 nM (at S/N = 3). The electrochemical sensor can also be applied for the quantitative analysis of dopamine in real samples. Graphical abstract A novel electrochemical sensing platform based on gold nanobipyramid/multi-walled carbon nanotube hybrids (AuNBP/MWCNTs) was proposed to detect dopamine in the presence of ascorbic acid and uric acid.

Optical fiber surface defects have diverse complicated features and different influencing factors. Therefore, the surface defect detection method for optical fiber has good generalization performance. Aiming at the problems of low efficiency, long detection time and high false detection rate in the traditional detection methods of fiber defects on the production line, we establish a database containing three kinds of surface defect samples on the fiber and augmented it in order to reduce over-fitting. This paper proposes a fiber surface detection method based on DSSD algorithm. In the convolutional neural network, the basic network ResNet-101 is utilized to enhance the network feature extraction capability and improve the robustness of the algorithm. The experimental data shows that the detection rate based on DSSD algorithm can reach 96.7%, which proves that the designed fiber intelligent defect detection method can not only greatly reduce the detection time, but also improve the detection efficiency and detection accuracy.

In the visual inspection of flexible materials such as textile surface, the amount of background information is too large, which leads to the problem that the key features need to be extracted is not obvious. The focal loss function is used to construct the RetinaNet and quickly filter out background information, so that the feature information is obtained faster, that is, the surface is flawed. RetinaNet using focal loss is a popular one-stage neural network model in recent years. Based on the feedforward residual network (ResNet), the feature pyramid network (FPN) is used to generate rich multi-scale convolution feature pyramids, which can quickly acquire The characteristics required are important for improving the detection speed of the surface of flexible materials.

Air pollution control is important work and realizing the effect of treatment has a significance on follow-up policy-making and perfection of closed-loop management. Based on the principle of post control, the article constructs a comprehensive decision-making framework for air pollution control effect evaluation. Firstly, considering the impact of sustainable development, the evaluation index system is established from environmental, social and economic aspects. Secondly, the improved hesitant fuzzy linguistic set, fuzzy analytical network process and cloud fuzzy synthetic evaluation are employed as core methods to determine the air pollution control effect. Then, the decision framework is applied in a case study and the result shows that the effect of air pollution control in the region is at a good level. Further, the stability and applicability of the framework are proved through sensitivity and comparative analysis. Finally, suggestions to improve the effectiveness of air pollution control are put forward.

In this study, considering break the original energy structure dominated by coal and make the most use of renewable energy in the future, an inexact stochastic robust mixed-integer programming (ITSRMP) method was developed for supporting regional electric power system management in Tangshan City, China. The developed method incorporated interval-parameter programming (IPP), stochastic robust optimization (SRO), two-stage stochastic programming (TSP), and mixed integer programming (MIP) within a general optimization framework to reflect uncertainties expressed as interval values and probability distributions in the regional electric power system. Three scenarios corresponding to different subsidy price levels and three cases associated with different pollutants emission reduction levels were designed. The electricity generation schemes, facility-expansion, pollutant emission, and system cost considering the subsidy policy and air pollution mitigation control had been obtained. The results indicated that subsidy policy would exert an important influence on the development of Tangshan's electric power system, which can reduce the cost advantage of conventional power generation and enhance the development enthusiasm of renewable power generation to power enterprises. In detail, the electricity generation amount of renewable energy would increase with the improvement of subsidy price level. Moreover, decision makers could identify the possible policy implementations and enforcements under considering the trade-off among system economy, security and environmental objectives. The modeling results were valuable for promoting new energy accommodation and supporting the sustainable development of social economy.

In this study, an inexact fuzzy bi-level programming model was developed for regional energy and traffic system management under uncertainty in Urumqi city, China. The energy system and traffic system are important subsystems of regional areas such as cities. The coordinated management of regional subsystems is a difficult problem for regional management. A bi-level programming model is an appropriate and simple method to describe the coordinated management of regional subsystems. The energy and traffic structure adjustment, clean power generation and pollutant emission–reduction targets are designed to support the construction of an environmentally sustainable city in China. Methods of interval parameter programming and bi-level programming were incorporated into the developed model to tackle uncertainties and reflect the features in the system. The environmental impacts of energy and traffic activities and policies were analysed. The results are valuable for supporting the management or justific...

Location decision of agroforestry biomass cogeneration (AFBC) project belongs to macro site selection, which is an important task in the pre-project planning stage and directly affects the subsequent economic benefits of the project. However, there are few studies on the site selection of biomass cogeneration projects. And the existing research equates the site selection of biomass cogeneration projects with that of biomass power generation projects, without distinguishing between the two, which is unreasonable. Therefore, in order to solve the above problems, this paper proposes a location decision framework for the AFBC project based on multi-attribute decision-making (MADM) method. Firstly, a special evaluation criteria system is constructed for the location decision of the AFBC project, which includes three main criteria consisting of social needs, resource supply and economic factors, as well as 11 sub-criteria. Then, considering the strong independence of the criteria in the AFBC project location decision, the subjective and objective weights are determined by best-worst method (BWM) and entropy method respectively. And then multiplicative integration method is applied to calculate the comprehensive weights of the criteria to ensure the rationality. Afterwards, Multi-Attributive Border Approximation area Comparison (MABAC) method is used to rank alternative locations and select the optimal. Finally, a case from Hebei Province of China is illustrated to verify the feasibility and validity of this location decision framework in practical applications.