With the research and the development of graphene-based materials, new sensors based on graphene compound materials are of great significance to scientific research and the consumer market. However, ...in the past ten years, due to the requirements of sensor accuracy, reliability, and durability, the development of new graphene sensors still faces many challenges in the future. Due to the special structure of graphene, the obtained characteristics can meet the requirements of high-performance sensors. Therefore, graphene materials have been applied in many innovative sensor materials in recent years. This paper introduces the important role and specific examples of sensors based on graphene and its base materials in biomedicine, photoelectrochemistry, flexible pressure, and other fields in recent years, and it puts forward the difficulties encountered in the application of graphene materials in sensors. Finally, the development direction of graphene sensors has been prospected. For the past two years of the COVID-19 epidemic, the detection of the virus sensor has been investigated. These new graphene sensors can complete signal detection based on accuracy and reliability, which provides a reference for researchers to select and manufacture sensor materials.
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•We analyse the scarcity-weighted fossil fuel footprint of China at the provincial level.•A scarcity evaluation indicator is introduced to reflect relative scarcity of fossil fuel ...flows.•Interregional export is a major driver of fossil fuel depletion in less-developed regions.•Our study can help Chinese regions to identify fossil fuel supply risk.
Supply risks and shortages of fossil fuels are major challenges to the sustainable development of countries. In response to this challenge, the 12th main goal of the Sustainable Development Goals emphasizes the importance of sustainable consumption and production patterns for resource (including fossil fuels) sustainability. However, for China, the world's largest energy consumer, the availability and criticality of fossil fuels to economic development have not been studied at sub-national scales. Understanding these can help fossil fuel management and the implementation of policies in different regions. This study is the first to analyse the scarcity-weighted fossil fuel footprint in China at the provincial level for 2012 using an environmentally extended multi-regional input-output model and a newly proposed scarcity evaluation indicator. Using scarcity-weighted indicators allows us to identify supply insecurities that are not revealed when focusing on fossil fuel extraction. The scarcity-weighted fossil fuel indicators identify new critical regions such as Hunan and Hubei. We also find that interprovincial export is a major driver of fossil fuel depletion in less-developed regions (e.g., 83% for Shanxi). This study can help regions in China identify fossil fuel supply risks from the viewpoint of their natural capital endowment and resource depletion in relation to final demand. More importantly, the research findings provide a valuable reference for policymakers when reassessing sustainability not only for fossil fuels but also for other natural resources at multiple scales within and beyond China.
Mangroves are an important source of blue carbon that grow in coastal areas. The study of mangrove species distribution is the basis of carbon storage research. In this study, we explored the ...potential of combining optical (Gaofen-1, Sentinel-2, and Landsat-9) and fully polarized synthetic aperture radar data from different periods (Gaofen-3) to distinguish mangrove species in the Fucheng town of Leizhou, Guangdong Province. The Gaofen-1 data were fused with Sentinel-2 and Landsat-9 satellite data, respectively. The new data after fusion had both high spatial and spectral resolution. The backscattering coefficient and polarization decomposition parameters of the fully polarized SAR data which could characterize the canopy structure of mangroves were extracted. Ten different feature combinations were designed by combining the two types of data. The extremely randomized trees algorithm (ERT) was used to classify the species, and the optimal feature subset was selected by the feature selection algorithm on the basis of the ERT, and the importance of the features was sorted. Studies show the following: (1) When controlling a single variable, the higher the spatial resolution of the multi-spectral data, the higher the interspecific classification accuracy. (2) The coupled Sentinel-2 and Landsat-9 data with a 2 m resolution will have higher classification accuracy than a single data source. (3) The selected feature subset contains all types of features in the optical data and the polarization decomposition features of the SAR data from different periods: multi-spectral band > texture feature > polarization decomposition parameter > vegetation index. Among the optimized feature combinations, the classification accuracy of mangrove species was the highest, the overall classification accuracy was 90.13%, and Kappa was 0.84, indicating that multi-source and SAR data from different periods coupling could improve the discrimination of mangrove species. (4) The ERT classification algorithm is suitable for the study of mangrove species classification, and the classification accuracy of extremely random trees in this paper is higher than that of random forest (RF), K-nearest neighbor (KNN), and Bayesian (Bayes). The results can provide technical guidance and data support for mangrove species monitoring based on multi-source satellite data.
Though effective in theoretical simulation, the established traffic control models and optimization algorithms will result in model mismatch or even control strategy failure in actual application. ...However, they are commonly adopted in traffic signal control research, resulting in the unavailability of many exceptional control algorithms in practice. Simulation should function as a bridge between theoretical research and actual application, allowing the gap between the two to be communicated and made up for. However, an effective connection between the two has yet to be established to enable simulation methods in existing traffic control research. To this end, we designed and developed a simulation platform for "Online Application-HILS (Hardware-in-the-Loop Simulation)-Practice" integration over traffic signal control. In this paper, the architecture and characteristics of the integrated simulation platform were described. Besides, the function of each module of the platform was detailed, followed by listing simulation examples for six complex scenarios, with the active control scenario being selected for simulation comparison analysis. The findings demonstrated extensive road network simulation with the integrated simulation platform, multidimensional control variables, control strategies with support, as well as stable and reliable operation. It can be used to verify several sorts of traffic control simulation with variable dimensions.
The dynamic-scheduling problem of transmission tasks (DSTT) is an important problem in the daily work of radio and television transmission stations. The transmission effect obtained by the greedy ...algorithm for task allocation is poor. In the case of more tasks and equipment and smaller time division, the precise algorithm cannot complete the calculation within an effective timeframe. In order to solve this problem, this paper proposes a discrete particle swarm optimization algorithm (DPSO), builds a DSTT mathematical model suitable for the DPSO, solves the problem that particle swarm operations are not easy to describe in discrete problems, and redefines the particle motion strategy and adds random disturbance operation in its probabilistic selection model to ensure the effectiveness of the algorithm. In the comparison experiment, the DPSO achieved much higher success rates than the greedy algorithm (GR) and the improved genetic algorithm (IGA). Finally, in the simulation experiment, the result data show that the accuracy of the DPSO outperforms that of the GR and IGA by up to 3.012295% and 0.11115%, respectively, and the efficiency of the DPSO outperforms that of the IGA by up to 69.246%.
Transmission task static allocation (TTSA) is one of the most important issues in the automatic management of radio and television stations. Different transmission tasks are allocated to the most ...suitable transmission equipment to achieve the overall optimal transmission effect. This study proposes a TTSA mathematical model suitable for solving multiple intelligent algorithms, with the goal of achieving the highest comprehensive evaluation value, and conducts comparative testing of multiple intelligent algorithms. An improved crossover operator is proposed to solve the problem of chromosome conflicts. The operator is applied to improved genetic algorithm (IGA) and hybrid intelligent algorithms. A discrete particle swarm optimization (DPSO) algorithm is proposed, which redefines the particle position, particle movement direction, and particle movement speed for the problem itself. A particle movement update strategy based on a probability selection model is designed to ensure the search range of the DPSO, and random perturbation is designed to improve the diversity of the population. Based on simulation, comparative experiments were conducted on the proposed intelligent algorithms and the results of three aspects were compared: the success rate, convergence speed, and accuracy of the algorithm. The DPSO has the greatest advantage in solving TTSA.
Marine oil spill causes severe damage to the marine ecological environment. Synthetic aperture radar (SAR) is widely used in marine oil spill detection due to its all-day and all-weather advantages. ...However, long stripe shape oil spill areas make it challenging to extract the oil spills effectively. A multifeature semantic complementation network (MFSCNet) is proposed for oil spill localization and segmentation of SAR images in one framework to address these problems. The long strip shape interference of oil spill is reduced by extracting intensity and damping ratio characters from nonpolarimetric features and entropy, anisotropy, and mean scattering angle from polarization features to form a multifeature SAR image. Then, the backbone feature network and feature fusion module are used for feature extraction. The decoupled head and the proposed oil spill semantic segmentation head are used for localization and semantic segmentation tasks, respectively. Also, the semantic complementation module is used in the training phase. It combines the results of localization and semantic segmentation to obtain complementation boxes for interactive iterative updating of the model parameters to enhance the detection accuracy of localization boxes. The effectiveness of the proposed model is demonstrated based on a lot of Sentinel-1 oil spill data compared with other state-of-the-art methods.
Environmental pressures have rapidly increased in various regions worldwide due to globalization. Thus, sustainable consumption and production are crucial for sustainable resource development. The ...material footprint (MF) of 180 countries was calculated from 1995 to 2015, and spatial autocorrelation analysis was conducted to investigate the spatiotemporal trend of the global MF. The results show that the global MF presented an upward trend from 1995 to 2015, increasing by 83%, and we find that the global per capita MF exhibits clustering, with an increasing trend during the study period. The findings indicate that resource consumption is similar in neighboring areas, especially in countries with a high MF surrounded by countries with a high MF (high-high clustering) and countries with low-low clustering. In addition, the number of countries with high clustering increased during the study period. We should take advantage of clustering, improve resource utilization, increase the technical carrying capacity, and develop energy-saving technologies. In African regions with low-low clustering, the economy of the surrounding areas should be stimulated to strengthen economic and technological clustering. In addition, advanced technology should be incorporated to improve the efficiency of using natural resources. This study can provide a reference for the spatial distribution of sustainable resource development.
Quinoa straw is rich in hemicellulose, and it could be hydrolyzed into xylose. It is a promising energy resource alternative that acts as a potential low-cost material for producing xylitol. In this ...study, quinoa straw was used as a substrate subjected to the hydrolysis of dilute sulfuric acid solution. Based on the production of xylose and inhibitors during hydrolysis, the optimal conditions for the hydrolysis of hemicellulose in quinoa straw were determined. Detoxification was performed via activated carbon adsorption. The optimal detoxification conditions were determined on the basis of major inhibitor concentrations in the hydrolysate. When the addition of activated carbon was 3% at 30 °C for 40 min, the removal of formic acid, acetic acid, furfural, and 5-HMF could reach 66.52%, 64.54%, 88.31%, and 89.44%, respectively. In addition to activated carbon adsorption, vacuum evaporation was further conducted to perform two-step detoxification. Subsequently, the detoxified hydrolysate was used for xylitol fermentation. The yield of xylitol reached 0.50 g/g after 96 h of fermentation by
(CICC 1779). It is 1.2-fold higher than that obtained through the sole vacuum evaporation method. This study validated the feasibility of xylitol production from quinoa straw via a biorefinery process.
Due to the great influence of multipath effect, noise, clock and error on pseudorange, the carrier phase double difference equation is widely used in high-precision indoor pseudolite positioning. The ...initial position is determined mostly by the known point initialization (KPI) method, and then the ambiguities can be fixed with the LAMBDA method. In this paper, a new method without using the KPI to achieve high-precision indoor pseudolite positioning is proposed. The initial coordinates can be quickly obtained to meet the accuracy requirement of the indoor LAMBDA method. The detailed processes of the method follows: Aiming at the low-cost single-frequency pseudolite system, the static differential pseudolite system (DPL) method is used to obtain the low-accuracy positioning coordinates of the rover station quickly. Then, the ambiguity function method (AFM) is used to search for the coordinates in the corresponding epoch. The real coordinates obtained by AFM can meet the initial accuracy requirement of the LAMBDA method, so that the double difference carrier phase ambiguities can be correctly fixed. Following the above steps, high-precision indoor pseudolite positioning can be realized. Several experiments, including static and dynamic tests, are conducted to verify the feasibility of the new method. According to the results of the experiments, the initial coordinates with the accuracy of decimeter level through the DPL can be obtained. For the AFM part, both a one-meter search scope and two-centimeter or four-centimeter search steps are used to ensure the precision at the centimeter level and high search efficiency. After dealing with the problem of multiple peaks caused by the ambiguity cosine function, the coordinate information of the maximum ambiguity function value (AFV) is taken as the initial value of the LAMBDA, and the ambiguities can be fixed quickly. The new method provides accuracies at the centimeter level for dynamic experiments and at the millimeter level for static ones.
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