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This study explores methods to assess the benefits of WPHPP from three aspects: economic, environmental, and social. Decarbonization has become an inevitable trend in the electricity generation industry. Storage integrated renewable energy generation plants have enhanced the stability of the output of renewable energy to a certain degree. Wind power hydrogen production projects (WPHPPs) could be a promising solution to the Chinese government’s peak carbon dioxide emissions goal. To achieve this goal, an evaluation system consisting of 12 main benefits factors is established. Then, an assessment approach based on Cloud-MULTIMOORA is proposed to select the best alternative. Specifically, HELTS-Cloud model is used to describe the information given by decision-makers and experts. Then, the SWARA and CRITIC methods are applied to obtain the subjective and objective weights. Additionally, a constrained optimization model is introduced to obtain the comprehensive weights. Finally, the MULTIMOORA-EDA method is used to obtain the final ranking of alternatives. Additionally, a case study is made to certify the usability and feasibility of the proposed method. This paper provides a method for assessing the benefits of WPHPP and promotes the application of this new kind of green energy project in the future.

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.

This study reviewed evidence linking economic globalization to the obesity epidemic. Keyword/reference search was performed in PubMed, Web of Science, Cochrane Library, Scopus, EconLit, Google Scholar, and BMC Globalization and Health. A total of 16 studies were identified, in which six adopted a cross-sectional study design, nine adopted a longitudinal study design, and the remaining one adopted a case-control study design. Thirteen studies assessed the relationship between economic globalization and obesity at the country level, whereas the remaining three analyzed individual-level data. Fourteen studies found at least one aspect and/or measure of economic globalization to be positively associated with overweight/obesity, one found an inverse association, and the remaining one reported a null finding. Through market deregulation, tariff reduction, and investment liberalization, economic globalization tends to accelerate the market entry of modern food manufacturers, supermarket chains, and fast-food restaurants, resulting in substantially increased supply of high-sugar/fat energy-dense foods with enhanced variety and accessibility and reduced price. The potential impact of economic globalization on obesity through the adoption of modern workplace and domestic technologies and motorized transportation and through changes in social norms and culture were hypothesized in the literature but not empirically examined, which warrants future data-driven research.

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.

For the problem of image distortion in textile flaw detection, a super-resolution image reconstruction technique based on GAN (Generative adversarial network) can reconstruct the obtained low-pixel image into a high-pixel image. The generative adversarial network consists of a discriminative network and a generative network. Generative network is responsible for generate high-resolution images, discriminative network is responsible for identifying the authenticity of the image. the generative loss and discriminative loss continuously optimize the network and guide the generation of high-quality images. The experimental results show that, the PNSR of SRGAN is 0.83 higher than that of the Bilinear, and the SSIM is higher than 0.0819. SRGAN can get a clearer image and reconstruct a richer texture, more high-frequency details, and easier to identify defects, which is important in the flaw detection of fabrics.

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...

Multi-criteria decision-making (MCDM) method has a widely application in management and energy field. Considering the broad development prospects of offshore wind power and deficiency of integrated coastal management, a decision framework combining triangular intuitionistic fuzzy numbers (TIFNs), Analytic Network Process (ANP) and Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE) is proposed and applied in site selection of offshore wind power station (OWPS). The aim of this study is to provide theoretical and methodological support for the site selection decision-making of coastal wind power projects and to improve the benefits of integrated coastal management. Taking six criteria (wind resources, construction, economic, environment, society, risk) and the related sub-criteria into consideration, an evaluation system of OWPS site selection is established. The optimal location scheme is determined by the decision framework in current paper. After a sensitivity analysis and a comparative analysis, the result shows that decision framework has strong robustness and feasibility. Thus, the evaluation criteria and methodology in this paper can provide a theoretical reference for the development of coastal management and offshore wind power.

Noble metal network nanostructures with three-dimensionally (3D) interconnected architectures are attracting much attention because of their unique catalytic, electrical, and optical properties. In this work, we report a facile ethylenediaminetetraacetic acid (EDTA) mediated chemical reduction route for the synthesis of high-quality palladium (Pd) nanowire networks (Pd-NWNWs). During the synthesis, EDTA interacts with PdCl2 to generate EDTA-PdII complex, which efficiently decreases the reduction potential of PdII precursor. The slow reduction rate is crucial for the generation of Pd nanowires. The morphology, composition, and structure of Pd-NWNWs are fully investigated by various physical characterizations. The electrocatalytic activity of Pd-NWNWs for the hydrazine oxidation reaction (HOR) is also studied by cyclic voltammetry, electrochemical impedance spectroscopy, and chronoamperometry, etc. Compared to commercial Pd black, Pd-NWNWs exhibit remarkably improved electrocatalytic activity and stability for the HOR.

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.

Nanocrystalline silver dressings are widely known to be potent antimicrobial and anti‐inflammatory agents and have long been used to treat topical wounds. Gold is known to be a strong anti‐inflammatory agent and has been used in the treatment of rheumatoid arthritis for >70 years. The purpose of this work was to study the effect of incorporating gold into nanocrystalline silver dressings from antimicrobial and anti‐inflammatory perspectives. Gold and silver dressing alloys were created by direct current magnetron sputtering and compared with pure silver nanocrystalline dressings using conventional biological (log reduction and corrected zone of inhibition) and physical (X‐ray diffraction, X‐ray photoelectron spectroscopy, energy‐dispersive X‐ray spectroscopy, atomic absorption spectroscopy, atomic force microscopy and scanning electron microscopy) characterisation techniques. While the gold/silver dressings were slightly weaker antimicrobials than the pure silver nanocrystalline structures, the addition of gold to the nanostructure reduces the minimum crystallite size from 17 to 4 nm. This difference increases the number of grain boundary atoms from 12% to 40% which could augment the anti‐inflammatory properties of the dressings. The formation of gold oxide (Au(2)O(3)) was thought to be responsible for the observed decrease in crystallite size.