High-sensitivity luminescent temperature sensors: MFX:1%Sm (M = Sr, Ba, X = Cl, Br).

The use of lanthanide luminescence has advanced the field of remote temperature sensing. Luminescence intensity ratio methods relying on emission from two thermally coupled energy levels are popular but suffer from a limited temperature range. Here, we present a versatile luminescent thermometer: Ba(Sr)FBr(Cl):Sm.

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.

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".

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 (FeCl⋅6HO) 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.

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.

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.

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.

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.