Taiyuan University of Technology

In this paper, the detailed steps for the derivation of linearized minimum current stress (LMCS) modulation scheme for single-phase bidirectional and isolated dual active bridge (DAB) ac-dc converters is presented. In order to reduce the current stress of the converter, a Lagrange multiplier method (LMM) scheme is designed to obtain the control coordinates constraint on the minimum value of the current stress. Compared with the conventional minimum current stress (MCS) control scheme, the proposed scheme realizes the linearization of power control, which significantly reduces the complexity of the controller algorithm. Moreover, the synchronous inverter control applied to the back-stage full-bridge is designed. Thus, the single stage power conversion is realized and the converter topology is electrolytic capacitorless, which promote the improvement of system reliability and power density. The impact of the ZVS scheme is analyzed in detail, and the adaptive-ZVS scheme for less system loss is selected. Finally, experimental results confirm the validity and feasibility of the proposed control scheme.

In phase-sensitive optical time-domain reflectometry (ϕ-OTDR) systems, DCM and Arctan demodulation algorithms are typically used to recover the information on vibration events along the fiber. The amplitude-frequency response characteristics of the DCM and Arctan algorithms are investigated and compared using a 3×3 coupler demodulation technique. We first testify the demodulation capability of the DCM and Arctan algorithms through experiments in ϕ-OTDR. Then the amplitude-frequency response ranges of the two algorithms are analyzed according to the demodulated phase error distribution from both the experiment and simulation. It is shown that the Arctan algorithm has a wider amplitude-frequency range than the DCM algorithm. The phase error generation mechanism is also investigated and we find that, in a noise-free condition, the phase errors still appear with use of the DCM algorithm, which cannot be explained by the general perspective that the phase errors arise from the sensitivity of the differential operator to noise. For both algorithms the phase error is related to the amplitude (D) and frequency (f) of the vibration signal, and pulse repetition frequency $(f_pulse)$ . When the phase error tolerance is less than 0.1, the expression of the amplitude-frequency response characteristic of DCM algorithm is 5.7 $(D+1)f\leq f_pulse$ , whereas that of the Arctan algorithm is $2Df\leq f_pulse$ .

A joint algorithm of wavelength domain differential accumulation (WDDA) and local cross-correlation is proposed in an optical frequency domain reflectometry (OFDR) for fast strain measurement along the sensing fiber. Among this, the WDDA is used to realize rapid strain positioning first, then the local cross-correlation is operated to the strain region only for fast strain computation. Thus, the processing time is substantially decreased. The mathematical principle of WDDA is theoretically derived and verified by numerical simulation. Meanwhile, the time complexity of WDDA is demonstrated to be reduced from O(nlogn) to O(n) by theoretical analysis and experimental verification, compared with the traditional whole-region cross correlation. Experiments are carried out to verify the effectiveness of this method. In experiments, we exerted the same strain region over 10.6, 35.0, 63.5, and 113.3 m long fibers. The strain greater than 10 μϵ can be identified by the WDDA. Compared with the traditional cross-correlation method in OFDR, the processing speed is increased by 5.3, 6.1, 6.3 and 6.4 times, respectively.

A denoising method combined with singular value decomposition (SVD) and Variational mode decomposition (VMD) is proposed to eliminate noise in on-site partial discharge (PD) signals from high-voltage electrical equipment. In the Fourier transform power spectrum, periodic narrowband interference was eliminated after SVD was offered to determine the number of singular values of periodic narrowband interference. Then, the PD signal was decomposed into K intrinsic mode function components by VMD. The empirical mode decomposition method was used to determine the K value of white noise’s intrinsic mode function components. An improved 3σ criterion threshold method was proposed to eliminate the residual noise in the PD signal. The denoising method was compared with the other two methods to analyze the denoising effect on the simulation and experimental PD signal. This paper’s denoising method can eliminate periodic narrowband interference and white noise in different PD signals of cavity discharge, corona discharge, and those from the motors’ coil. The denoised PD pulse waveform has a higher signal-to-noise ratio and normalized correlation coefficient and a lower mean square error, indicating that the waveform’s original characteristic remains.

Multivariate time series forecasting is an important issue in industries, agriculture, finance, and other applications. There are many challenging problems in it such as non-linear and complicated relationships among the series. The entanglement of latent multiple different sequence patterns maybe one of the most reasons for the time series complex behavior, and decomposition can help reveal the hidden evolution law. Graph is a good modelling tool for multiple entities and graph neural network has showed better learning ability for spatial dependence, but its high memory consumption requires valid solutions. Inspired by the above two points, we propose a Temporal Decomposition enhanced Graph neural network for Multivariate time Series Forecasting, namely TDG4MSF, which mainly consists of four components: temporal decomposition enhanced representation learning, graph structure learning, gated GNNML-based representation learning and MLP-based forecasting. A progressive quadratic decomposition architecture is designed in the temporal decomposition enhanced representation learning that extracts the different periodic patterns of time series. Graph structure learning is used to construct adjacency matrix to represent the spatial topological structure. The temporal decomposition enhanced representation and adjacency matrix are fed into gated GNNML to integrating temporal and spatial information between variables. A MLP-based forecasting is utilized to make multivariate time series prediction. Experimental results show the effectiveness of our model in short- and medium-term prediction scenarios are superior to the state-of-the-art methods, which help make exact decisions timely. Code is available at: https://github.com/TYUT-Theta/MHZN.git.

In this paper, a class of variable coefficient coupled Schrödinger equations with gain or loss terms is studied, which can be used to describe soliton excitation in non-uniform trihydrogen chain α\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha $$\end{document}-helix proteins. The N-soliton solutions of this equation are obtained by using the Hirota bilinear method, the 1,2, 3-soliton solutions are numerically simulated, and their dynamic properties are analyzed. On this basis, the asymptotic behavior of soliton solutions is discussed. Besides, the influence of variable coefficient function and other parameters on solitons is studied, the corresponding rules are summarized.

Over time, the economy’s growth, financial development, and environmental taxes have become vital tools in countering ecological degradation and promoting clean energy. However, there needs to be a research gap in assessing these policies’ collective impact on renewable energy adoption, especially in developing West African countries. This study addresses this gap by evaluating the effectiveness of these policies from 1990 to 2020, using the Generalized Method of Moments (GMM), fixed effect, and pooled Ordinary Least Squares (OLS) models. The Dumitrescu-Hurlin panel causality test reveals bidirectional causality between economic growth and renewable energy consumption, as well as between financial development and renewable energy use. Unidirectional causality is found from environmental tax to renewable energy consumption. GMM results highlight the positive influences of economic growth and environmental taxes on renewable energy consumption, while financial development negatively affects it. These outcomes are consistent with fixed effect and pooled OLS models. Sectorial heterogeneity analysis indicates better results for countries with strong institutions, advanced technology, and strict regulations. In conclusion, this study’s insights can guide policies for sustainability in West Africa, leveraging economic growth, environmental taxes, and technology for effective renewable energy integration. Graphical abstract

In this paper, anionic branched-chain tertiary fatty alcohol sulfate surfactants were synthesized from n -hexene and n -octene using selective olefin dimerization, hydration reaction (hydroxylation), and sulfur trioxide sulfation. The direct hydration reaction of the α-olefin dimer 2-butyl-1-octene with water as a model reaction was explored for the synthesis of branched-chain tertiary fatty alcohols. Two branched-chain tertiary fatty alcohol sulfate surfactants, namely C 12 -SBTAS and C 16 -SBTAS, with different carbon chain lengths, were synthesized by the sulfur trioxide sulfation method. Their structures were confirmed by various analytical techniques, including HPLC, FT-IR, HR-MS, and ¹ H NMR. Equilibrium and dynamic surface tension, foaming, wetting, and emulsifying properties were compared with those of Guerbet cetyl alcohol sulfate (C 16 -SGAS). C 12 -SBTAS and C 16 -SBTAS exhibited good surface activity with equilibrium surface tension ( γ CMC ) values of 27.41 mN m ⁻¹ and 26.69 mN m ⁻¹ , respectively. They also had low foaming and rapid defoaming abilities, as well as good wetting and emulsifying properties, which match the application characteristics of typical branched-chain surfactants.

In the context of the green and low-carbon development era, the influence of circulating fluidized bed fly ash (CFBFA) replacement of cement on the expansion properties of paste under different curing conditions was first systematically investigated. Then, a recycled aggregate self-compacting concrete-filled steel tubes (CFSTs) with CFBFA was prepared. The experimental results show that because of its unique physicochemical properties, the incorporation of CFBFA will significantly increase the water consumption to achieve a standard consistency of the paste, and the pore structure can be optimized, the number of large pores can be reduced due to the formation of more expansion components, ettringite, gypsum and portlandite are the main expansive products of CFBFA-cement paste, and the influence of factors such as curing conditions, age and CFBFA on the expansion effect is notable. With a 40% CFBFA content, the recycled aggregate self-compacting CFSTs showed 41 μm/m expansion under closed curing conditions, while the 28-day compressive strength reached 48.9 MPa.

High-capacity O3-type layered sodium oxides have been considered one of the most promising cathode materials for the next generation of Na-ion batteries (NIBs). However, these cathodes usually suffer from low high-rate capacity and poor cycling stability due tostructure deformation, native air sensitivity, and interfacial side reactions. Herein, a multi-site substituted strategy was employed to enhance the stability of O3-type NaNi0.5Mn0.5O2. Simulations indicated that the Ti substitution decreased the charge density of Ni ions and improved the antioxidative capability of the material. In addition, the synergistic effect of K+ and Ti4+ significantly reduced the formation energy of Na+ vacancy and delivered an ultra-low lattice strain during the repeated Na+ extraction/insertion. In-situ characterizations verified that the complicated phase transformation was mitigated during the charge/discharge process, resulting in greatly improved structure stability. The co-substituted cathode delivered a high�rate capacity of 97 mAh g-1 at 5 C and excellent capacity retention of 81% after 400 cycles at 0.5 C. The full cell paired with commercial hard carbon anode also exhibited high capacity and long cycling life. This dual-ion substitution strategy will provide a universal approach for the new rational design of high-capacity cathode materials for NIBs.

During past decades, water-based and oil-in-water (O/W) based nanolubricants with nanoparticles (NPs) as additives have been developed to replace conventional oil-based lubricants for solving contamination problems during rolling processes. The formulations of nanolubricants for rolling need to be continuously improved to provide better lubrication performance. This article reviews the formulation, physicochemical properties, and lubrication mechanisms during rolling with water-based and O/W based nanolubricants, and the effects of nanolubricants on rolling force, surface roughness and oxide skin formation during rolling are discussed. This review aims to facilitate the fundamental understanding of nanolubrication and the development of rolling lubrication technology.

Single‐atomic transition metal–nitrogen–carbon (M–N–C) structures are promising alternatives toward noble‐metal‐based catalysts for oxygen reduction reaction (ORR) catalysis involved in sustainable energy devices. The symmetrical electronic density distribution of the M─N4 moieties, however, leads to unfavorable intermediate adsorption and sluggish kinetics. Herein, a Fe–N–C catalyst with electronic asymmetry induced by one nearest carbon vacancy adjacent to Fe─N4 is conceptually produced, which induces an optimized d‐band center, lowered free energy barrier, and thus superior ORR activity with a half‐wave potential (E1/2) of 0.934 V in a challenging acidic solution and 0.901 V in an alkaline solution. When assembled as the cathode of a Zinc–air battery (ZAB), a peak power density of 218 mW cm⁻² and long‐term durability up to 200 h are recorded, 1.5 times higher than the noble metal‐based Pt/C+RuO2 catalyst. This work provides a new strategy on developing efficient M–N–C catalysts and offers an opportunity for the real‐world application of fuel cells and metal–air batteries.

Scattering problems have wide applications in the medical and military fields. In this paper, the weighted least-squares (WLS) collocation method based on radial basis functions (RBFs) is developed to solve elastic wave scattering problems, which are governed by the Navier equation and the Helmholtz equations with coupled boundary conditions. The perfectly matched layer (PML) technique is used to truncate the unbounded domain into a bounded domain. The WLS method is constructed by setting the collocation points denser than the trial centers and imposing different weights on different types of boundary conditions. The WLS method can overcome the matrix singularity problem encountered in the Kansa method, and the convergence rate of WLS is [Formula: see text] for Sobolev kernel with kernel smoothness [Formula: see text]. Furthermore, compared with the finite element method (FEM) and the Kansa method, WLS can provide higher accuracy and more stable solutions for relatively large angular frequencies. The numerical example with a circular obstacle is used to verify the effectiveness and convergence behavior of the WLS. Besides, the proposed scheme can easily handle irregular obstacles and obtain stable results with high accuracy, which is validated through experiments with ellipse and kite-shaped obstacles.

As a combination of modern imaging technology, stereotactic technology, computer technology, and artificial intelligence technology, the surgical navigation system has been rapidly developed and applied in recent years. In recent decades, navigation systems have been used to aid in maxillofacial surgery, but rarely in the oral area. By comparing the applications of navigation systems in oral and maxillofacial surgery, we summarize their advantages and problems and list the advantages and disadvantages of the development process and methods of Simultaneous Localization and Mapping(SLAM). We propose to use tooth contours as natural markers to construct a model of AR oral structure, subtracting the operational time problems caused by making and using artificial markers. The AR structure model of the oral cavity was constructed, the method of applying SLAM technology to the oral surgery was proposed reasonably, and the method optimization scheme was proposed for the more complicated oral root canal surgery.

Fiber optic communication technology is becoming increasingly important in various fields, and high-speed, large-capacity fiber optic transmission systems are urgently needed. The design of fiber amplifiers is influenced by both fiber length and doping concentration. Although the Erbium-doped fiber amplifier (EDFA) is one of the most widely used components in fiber optic communication systems, its working area cannot meet the growing demand for network traffic.This paper presents a new fiber amplifier operating in the spectral region of 1700-1800 nm, pumped by commercially available laser diodes, highlighting the potential benefits of developing optical amplifiers for new spectral regions.This paper constructs a model to optimize gain at the central wavelength by finding relevant parameters, fitting a binary function of gain as a function of doping concentration and fiber length using Matlab, and optimizing the maximum gain using a simulated annealing algorithm. The proposed fiber amplifier has potential applications in telecommunications, sensing, and spectroscopy. The design of the amplifier has been optimized to achieve high gain and low noise figures, which are crucial for practical applications. Furthermore, the amplifier can be easily integrated into existing fiber optic networks, making it a promising candidate for future optical communication systems.

Electrohydrodynamic (EHD) printing is a micro–nano printing technology based on the principles of electric field and fluid dynamics. It is characterized by high resolution, high precision, and high speed, applied to various materials, including metals, ceramics, and organic materials. Compared with traditional printing technologies, EHD printing offers advantages such as low manufacturing cost, simple process, and direct fabrication, making it highly promising in the field of micro–nano manufacturing. Polyethylene oxide (PEO) is a highly water‐soluble polymer that has been widely used in various fields due to its low toxicity and ease of processing. In this study, a finite element simulation model was developed using simulation software to simulate and analyze the mechanisms of focused jetting and deposition of PEO solution under an electric field. Based on the principles of electrohydrodynamics, a self‐built EHD printing system was used to investigate the influence of different solution mass fractions and printing parameters on fiber formation, and the optimal process window of EHD printing PEO solution was obtained. Ultimately, ordered deposition of fiber lines ranging from 1.761 to 6.093 μm was achieved. The simulation results were consistent with the experimental results, validating the effectiveness of the established model in guiding jetting outcomes. Highlights Independently building a low‐cost electrohydrodynamic (EHD) printing system. Finite element simulation of EHD printing process. Mechanism analysis of PEO solution jetting and deposition. Optimal process window for PEO solution EHD printing. Influence of key process parameters on fiber forming width.

Developing efficient and robust hydrogen evolution reaction (HER) catalysts for scalable and sustainable hydrogen production through electrochemical water splitting is strategic and challenging. Herein, heterogeneous Mo8O26‐NbNxOy supported on N‐doped graphene (defined as Mo8O26‐NbNxOy/NG) is synthesized by controllable hydrothermal reaction and nitridation process. The O‐exposed Mo8O26 clusters covalently confined on NbNxOy nanodomains provide a distinctive interface configuration and appropriate electronic structure, where fully exposed multiple active sites give excellent HER performance beyond commercial Pt/C catalyst in pH‐universal electrolytes. Theoretical studies reveal that the Mo8O26‐NbNxOy interface with electronic reconstruction affords near‐optimal hydrogen adsorption energy and enhanced initial H2O adsorption. Furthermore, the terminal O atoms in Mo8O26 clusters cooperate with Nb atoms to promote the initial H2O adsorption, and subsequently reduce the H2O dissociation energy, accelerating the entire HER kinetics.

Developing efficient and robust hydrogen evolution reaction (HER) catalysts for scalable and sustainable hydrogen production through electrochemical water splitting is strategic and challenging. Herein, heterogeneous Mo8O26‐NbNxOy supported on N‐doped graphene (defined as Mo8O26‐NbNxOy/NG) is synthesized by controllable hydrothermal reaction and nitridation process. The O‐exposed Mo8O26 clusters covalently confined on NbNxOy nanodomains provide a distinctive interface configuration and appropriate electronic structure, where fully exposed multiple active sites give excellent HER performance beyond commercial Pt/C catalyst in pH‐universal electrolytes. Theoretical studies reveal that the Mo8O26‐NbNxOy interface with electronic reconstruction affords near‐optimal hydrogen adsorption energy and enhanced initial H2O adsorption. Furthermore, the terminal O atoms in Mo8O26 clusters cooperate with Nb atoms to promote the initial H2O adsorption, and subsequently reduce the H2O dissociation energy, accelerating the entire HER kinetics.

Point cloud registration plays a critical role in many computer vision applications. Nevertheless, despite numerous feature-based registration methods that have been presented recently, the majority concerns learning local features, which unavoidably suffers from an insufficient discriminative ability of the point cloud feature descriptors. Thus, this paper proposes a more discriminative feature descriptor by combining global and local information and adding an intermediate supervision mechanism. Unlike previous methods, we introduce a Local-Nonlocal Module that focuses on the local information of the point cloud and captures the global information, thus improving the discriminative ability of the feature descriptors for repetitive structures. To obtain more robust keypoints, we utilize the progressive scoring mechanism to detect keypoints that are the most significant in the neighborhood and channels and range from a coarse to a detailed scope to provide progressive detectors (PD). Additionally, we utilize the multi-level supervision mechanism to provide stronger supervision signals. Finally, we train and evaluate the proposed model on the indoor dataset 3DMatch, with the experimental results indicating that our method outperforms related techniques.