Waste Detection System Based on Data Augmentation and YOLO_EC.

The problem of waste classification has been a major concern for both the government and society, and whether waste can be effectively classified will affect the sustainable development of human society. To perform fast and efficient detection of waste targets in the sorting process, this paper proposes a data augmentation + YOLO_EC waste detection system. First of all, because of the current shortage of multi-objective waste classification datasets, the heavy workload of human data collection, and the limited improvement of data features by traditional data augmentation methods, DCGAN (deep convolution generative adversarial networks) was optimized by improving the loss function, and an image-generation model was established to realize the generation of multi-objective waste images; secondly, with YOLOv4 (You Only Look Once version 4) as the basic model, EfficientNet is used as the backbone feature extraction network to realize the light weight of the algorithm, and at the same time, the CA (coordinate attention) attention mechanism is introduced to reconstruct the MBConv module to filter out high-quality information and enhance the feature extraction ability of the model. Experimental results show that on the HPU_WASTE dataset, the proposed model outperforms other models in both data augmentation and waste detection.

Data-Driven Bipartite Formation for a Class of Nonlinear MIMO Multiagent Systems.

The bipartite formation control for the nonlinear discrete-time multiagent systems with signed digraph is considered in this article, in which the dynamics of the agents are completely unknown and multi-input multi-output (MIMO). First, the unknown nonlinear dynamic is converted into the compact-form dynamic linearization (CFDL) data model with a pseudo-Jacobian matrix (PJM). Based on the structurally balanced signed graph, a distance-based formation term is constructed and a bipartite formation model-free adaptive control (MFAC) protocol is designed. By employing the measured input and output data of the agents, the theoretical analysis is developed to prove the bounded-input bounded-output stability and the asymptotic convergence of the formation tracking error. Finally, the effectiveness of the proposed protocol is verified by two numerical examples.

High Efficiency Adsorption Removal of Arsenilic Acid and Arsenate(V) by Iron-Modified Corncob Biochar.

It is crucial that a highly effective adsorbent can be used to simultaneously remove the composite pollution including both inorganic and organic arsenic from wastewater. In this work, the iron modified corncob biochar (MCCB), prepared via the co-precipitation of ferric chloride hexahydrate (FeCl3⋅6H2O) with sodium hydroxide (NaOH) on corncob biochar, was studied for the high efficiency removal of arsenilic acid (ASA) and arsenate [As(V)] in wastewater. X-ray diffraction, scanning electron microscopy, and fourier transform infrared spectroscopy were carried out to characterize the MCCB. At pH of 4.0-5.0, initial concentration of 10 mg/L ASA and 1 mg/L As(V), adsorbent dose of 0.4 g/L, the maximum adsorption capacities of ASA and As(V) were 49.20 and 4.89 mg/g, respectively. The adsorption performance of MCCB for ASA and As(V) was fitted well to the pseudo-second-order kinetic model. Results from this study indicate the promise of MCCB as an efficient, low-cost and environmentally friendly adsorbent for composite arsenic pollution.

Environmental Regulation Competition and Carbon Emissions.

To clarify the relationship between environmental regulatory competition and carbon emissions and provide a theoretical basis for carbon emission reduction governance, this paper explores the strategic interaction behavior of environmental regulatory competition by constructing a three-way evolutionary game model based on the perspective of the fusion of environmental federalism and local government competition theory. On this basis, the specific forms of carbon emission reduction competition are tested using the spatial Durbin model, and the mechanism of the effect of environmental regulation competition on carbon emissions is analyzed. The evolutionary game model shows that local governments make strategic choices based on the costs and benefits of environmental regulation, and there are strategic equilibria of "race to the bottom", "race to the top", and "differentiation of competition". The empirical results show that the competition for environmental regulations as a whole after the 18th National Congress of the Communist Party of China is a "race to the top", and the increase in the intensity of environmental regulations has an inhibitory effect on carbon emissions, which remains valid after a series of robustness tests. There is heterogeneity in environmental regulatory competition, and the effect of emissions reduction is most obvious in the central region. Mechanism analysis shows that environmental regulatory competition affects carbon emissions mainly through the effect of political performance assessment, the effect of industrial structure optimization, and the effect of low-carbon technology capability improvement. Therefore, the central government should follow the local government interest function and balance the interests of all parties, appropriately increase the proportion of environmental performance assessment and optimize the performance assessment system, and consider regional development differences to find the right carbon emissions reduction path.

Ensemble learning for prediction of the bioactivity capacity of herbal medicines from chromatographic fingerprints.

BACKGROUND: Recent quality control of complex mixtures, including herbal medicines, is not limited to chemical chromatographic definition of one or two selected compounds; multivariate linear regression methods with dimension reduction or regularisation have been used to predict the bioactivity capacity from the chromatographic fingerprints of the herbal extracts. The challenge of this type of analysis requires a multi-dimensional approach at two levels: firstly each herb comprises complex mixtures of active and non-active chemical components; and secondly there are many factors relating to the growth, production, and processing of the herbal products. All these factors result in the significantly diverse concentrations of bioactive compounds in the herbal products. Therefore, it is imminent to have a predictive model with better generalisation that can accurately predict the bioactivity capacity of samples when only the chemical fingerprints data are available. RESULTS: In this study, the algorithm of Stacking Multivariate Linear Regression (SMLR) and a few other commonly used chemometric approaches were evaluated. They were to predict the Cluster of Differentiation 80 (CD80) expression bioactivity of a commonly used herb, Astragali Radix (AR), from the corresponding chemical chromatographic fingerprints. SMLR provides a superior prediction accuracy in comparison with the other multivariate linear regression methods of PCR, PLSR, OPLS and EN in terms of MSEtest and the goodness of prediction of test samples. CONCLUSIONS: SMLR is a better platform than some multivariate linear regression methods. The first advantage of SMLR is that it has better generalisation to predict the bioactivity capacity of herbal medicines from their chromatographic fingerprints. Future studies should aim to further improve the SMLR algorithm. The second advantage of SMLR is that single chemical compounds can be effectively identified as highly bioactive components which demands further CD80 bioactivity confirmation..

An improved independent component analysis model for 3D chromatogram separation and its solution by multi-areas genetic algorithm.

BACKGROUND: The 3D chromatogram generated by High Performance Liquid Chromatography-Diode Array Detector (HPLC-DAD) has been researched widely in the field of herbal medicine, grape wine, agriculture, petroleum and so on. Currently, most of the methods used for separating a 3D chromatogram need to know the compounds' number in advance, which could be impossible especially when the compounds are complex or white noise exist. New method which extracts compounds from 3D chromatogram directly is needed. METHODS: In this paper, a new separation model named parallel Independent Component Analysis constrained by Reference Curve (pICARC) was proposed to transform the separation problem to a multi-parameter optimization issue. It was not necessary to know the number of compounds in the optimization. In order to find all the solutions, an algorithm named multi-areas Genetic Algorithm (mGA) was proposed, where multiple areas of candidate solutions were constructed according to the fitness and distances among the chromosomes. RESULTS: Simulations and experiments on a real life HPLC-DAD data set were used to demonstrate our method and its effectiveness. Through simulations, it can be seen that our method can separate 3D chromatogram to chromatogram peaks and spectra successfully even when they severely overlapped. It is also shown by the experiments that our method is effective to solve real HPLC-DAD data set. CONCLUSIONS: Our method can separate 3D chromatogram successfully without knowing the compounds' number in advance, which is fast and effective.

Application of genetic algorithm for discovery of core effective formulae in TCM clinical data.

Research on core and effective formulae (CEF) does not only summarize traditional Chinese medicine (TCM) treatment experience, it also helps to reveal the underlying knowledge in the formulation of a TCM prescription. In this paper, CEF discovery from tumor clinical data is discussed. The concepts of confidence, support, and effectiveness of the CEF are defined. Genetic algorithm (GA) is applied to find the CEF from a lung cancer dataset with 595 records from 161 patients. The results had 9 CEF with positive fitness values with 15 distinct herbs. The CEF have all had relative high average confidence and support. A herb-herb network was constructed and it shows that all the herbs in CEF are core herbs. The dataset was divided into CEF group and non-CEF group. The effective proportions of former group are significantly greater than those of latter group. A Synergy index (SI) was defined to evaluate the interaction between two herbs. There were 4 pairs of herbs with high SI values to indicate the synergy between the herbs. All the results agreed with the TCM theory, which demonstrates the feasibility of our approach.