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It is difficult for traditional circuit-fault feature-extraction methods to accurately distinguish between nonlinear analog-circuit faults and analog-circuit faults with high fault rates and high diagnostic costs. To solve this problem, this paper proposes a method of mathematical morphology fractal dimension (VMD-MMFD) based on variational mode decomposition for soft-fault feature extraction in analog circuits. First, the signal is decomposed into variational modes to suppress the influence of environmental noise, and multiple high-dimensional eigenmode functions with different center frequencies are obtained. The fractal dimension of the signal feature information component IMF is calculated, and then, KPCA (Kernel Principal Component Analysis) is used to remove the overlapping and redundant parts of the data. The fault set obtained is used as the basis for judging the working state and the fault type of the circuit. The experimental results of the simulation circuits show that this method can be effectively used for circuit-fault diagnosis.

Aiming at the problems of nonlinearity and serious confusion of fault characteristics in analog circuits, this paper proposed a fault diagnosis method for an analog circuit based on ensemble empirical pattern decomposition (EEMD) and improved multifractal detrended fluctuations analysis (MF-DFA). This method consists of three steps: preprocessing, feature extraction, and fault classification identification. First, the EEMD decomposition preprocesses (denoises) the original signal; then, the appropriate IMF components are selected by correlation analysis; then, the IMF components are processed by the improved MF-DFA, and the fault feature values are extracted by calculating the multifractal spectrum parameters, and then the feature values are input to a support vector machine (SVM) for classification, which enables the diagnosis of soft faults in analog circuits. The experimental results show that the proposed EEMD-improved MF-DFA method effectively extracts the features of soft faults in nonlinear analog circuits and obtains a high diagnosis rate.

Aiming at the problems of low reconstruction rate and poor reconstruction precision when reconstructing sparse signals in wireless sensor networks, a sparse signal reconstruction algorithm based on the Limit-Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) quasi-Newton method is proposed. The L-BFGS quasi-Newton method uses a two-loop recursion algorithm to find the descent direction dk directly by calculating the step difference between m adjacent iteration points, and a matrix Hk approximating the inverse of the Hessian matrix is constructed. It solves the disadvantages of BFGS requiring the calculation and storage of Hk, reduces the algorithm complexity, and improves the reconstruction rate. Finally, the experimental results show that the L-BFGS quasi-Newton method has good experimental results for solving the problem of sparse signal reconstruction in wireless sensor networks.

The improvement of coverage is a critical issue in the coverage hole patching of sensors. Traditionally, VOPR and VORCP algorithms improve the coverage of the detection area by improving the original VOR algorithm, but coverage hole patching algorithms only target homogeneous networks. In the real world, however, the nodes in the wireless sensor network (WSN) are often heterogeneous, i.e., the sensors have different sensing radii. The VORPH algorithm uses the VOR in a hybrid heterogeneous network and improves the original algorithm. The patched nodes are better utilized, and the detection range is enlarged. However, the utilization rate of the patched nodes is not optimized, making it impossible to patch the coverage holes to the maximum degree. In the environment of hybrid heterogeneous WSN, we propose a coverage hole patching algorithm with a priority mechanism. The algorithm determines the patching priority based on the size of the coverage holes, thereby improving network coverage, reducing node redundancy, and balancing resource allocation. The proposed algorithm was compared under the same environment by simulation and analysis. The results show that our algorithm is superior to the traditional coverage hole patching algorithms in coverage rate, and can reduce node redundancy.

Aiming at the problems of data loss and uneven energy consumption in wireless sensor networks during data transmission, this paper proposes a ReInForM transmission fault-tolerant routing algorithm based on energy selection and erasure code fault-tolerant machines (E-ReInForMIF). The E-ReInForMIF algorithm improves the multi-path routing algorithm by combining an erasure coding fault-tolerant machine and node residual energy sorting selection. First, the erasure coding fault-tolerant machine is used to encode the signal, determine the number of transmission paths through multi-path routing, and then select the specific node of the next hop by sorting the residual energy of the node. The E-ReInForMIF routing algorithm effectively solves the problems of uneven energy consumption and data loss in data transmission, improving network lifespan and transmission reliability. Finally, the signal is decoded. The simulation results show that the E-ReInForMIF routing algorithm is superior to the ReInForM routing algorithm in improving the reliability of data transmission.

The decoupling effect between economic growth and energy structure was quantitatively analyzed from 1999 to 2014 across China. The results showed it existed weak decoupling effects in most regions. Based on the analysis of the influence of energy structure on carbon intensity, using scenario simulation methods and Markov chain modeling, the carbon intensity was predicted for China in 2020. The impact of energy structure adjustment on the carbon intensity to meet China’s carbon target by 18 possible scenarios are calculated. Furthermore, the peak value of carbon emissions was also calculated in 2030. The results showed that the carbon intensity predicted for China in 2020 can be achieved regardless of whether the energy structure was adjusted or not when energy saving and carbon reduction policies maintained with economic growth at 6–7%. Moreover, given fixed energy structure growth, for each 1% of economic growth, the carbon intensity will decrease by about 3.5%. Given fixed economic growth, the decrease of energy intensity will be greater if the control of energy consumption is stronger. The effect of energy structure adjustment on the decreasing of carbon intensity will be 4% higher under constraints than without constraints. On average, the contribution of energy structure adjustment to achieving the carbon intensity target was calculated as 4% higher than that with constraints. In addition, given relatively fixed economic growth at 6–7%, the peak value of carbon emission in 2030 was calculated as 13.209 billion tons with constraints and 14.38 billion tons without constraints.

Acid mine drainage is an extraordinarily acidic and highly heavy metal ion-contaminated leachate, seriously threatening the environment. In this work, an industrial solid waste of steel slag is the adsorbent to remediate the simulated acid mine drainage containing a large amount of Fe(II) ions. Due to the excellent physicochemical properties and structures, steel slag exhibited remarkable Fe(II) removal performance. Its maximum removal efficiency was up to 100%. The initial pH, the dosage and particle size of steel slag, and initial concentration of heavy metal ions on Fe(II) removal efficiency were determined. The pseudo-second-order model and Freundlich isotherm model well described the adsorption behavior of steel slag, implying that the adsorption of Fe(II) by steel slag was mainly multilayer chemisorption. The thermodynamic study demonstrated that the adsorption process was endothermic and spontaneous; the enthalpy change was calculated to equal 91.21 kJ/mol. Mechanism study showed that the entire removal process of Fe(II) by steel slag was completed by electrostatic adsorption, chemical precipitation, and surface complexation in cooperation, and the chemical precipitation was the dominant mechanism. Meaningfully, this study provides a valuable strategy and path for engineering applications of AMD remediation by steel slag, which is prospective as an ideal candidate for Fe(II) ions elimination, inspiring the future development of “Treating the wastes with wastes.”

Basic oxygen furnace (BOF) slag was employed as a sorbent to treat acid mine drainage (AMD) composed of Fe(II), Mn(II), Zn(II), and Cu(II). Single-factor batch experiments, combined with characterization of the slag before and after adsorption, were conducted to evaluate its removal performance for each metal ion. In the AMD treatment process, the extraction efficiency of the metal ions decreased with increasing slag particle size and with higher initial heavy metal ions concentrations. The initial adsorption rates in competitive adsorption followed the order Fe(II) > Mn(II) ≥ Cu(II) > Zn(II). The extraction of Fe(II), Mn(II), Zn(II), and Cu(II) from the simulated AMD conformed to the pseudo-second-order kinetic model together with the Freundlich isotherm model, indicating that multilayer adsorption dominated and the active sites on the slag surface were inhomogeneously distributed.

Gymnocypris eckloni is widely distributed in isolated lakes and the upper reaches of the Yellow River and play significant roles in the trophic web of freshwater communities. In this study, we generated a chromosome-level genome of G. eckloni using PacBio, Illumina and Hi-C sequencing data. The genome consists of 23 pseudo-chromosomes that contain 918.68 Mb of sequence, with a scaffold N50 length of 43.54 Mb. In total, 23,157 genes were annotated, representing 94.80% of the total predicted protein-coding genes. The phylogenetic analysis showed that G. eckloni was most closely related to C. carpio with an estimated divergence time of ~34.8 million years ago. For G. eckloni , we identified a high-quality genome at the chromosome level. This genome will serve as a valuable genomic resource for future research on the evolution and ecology of the schizothoracine fish in the Qinghai-Tibetan Plateau. Measurement(s) Genome Technology Type(s) Whole Genome Sequencing Sample Characteristic - Organism Gymnocypris eckloni Sample Characteristic - Environment fresh water Sample Characteristic - Location Little Yellow River

This paper analyses the stress effect of cross-regional transfer of resource-exhausted enterprises from eastern China to central and Western China. A tripartite evolutionary game model including the central government, the local government of the operation recipient region, and the resource-exhausted transfer enterprises is established under the assumption of limited rationality. By analysing the evolutionary equilibrium and using MATLAB, for example, analysis, the relationship between equilibrium probability and various parameters, as well as the key influencing factors of equilibrium strategy were explored. The research shows, first, that the degree of punishment imposed by the central government on the local governments, the implementation of regulation by the local governments, and the amount of rewards/punishments implemented by the local governments for transfer enterprises are the key factors affecting evolutionary stability. Second, it shows that the local governments’ penalty for transfer enterprises has a significant impact on the convergence speed of enterprises’ strategic choice to “Completely Control Pollution.” Finally, from the perspective of the relationships between the central government and the local governments, as well as with transfer enterprises, countermeasures and suggestions are put forward to effectively prevent the stress effect of the cross-regional transfer behaviour of resource-exhausted enterprises.