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Floods are one of the most serious natural disasters that can cause huge societal and economic losses. Extensive research has been conducted on topics like flood monitoring, prediction, and loss estimation. In these research fields, flood velocity plays a crucial role and is an important factor that influences the reliability of the outcomes. Traditional methods rely on physical models for flood simulation and prediction and could generate accurate results but often take a long time. Deep learning technology has recently shown significant potential in the same field, especially in terms of efficiency, helping to overcome the time-consuming associated with traditional methods. This study explores the potential of deep learning models in predicting flood velocity. More specifically, we use a Multi-Layer Perceptron (MLP) model, a specific type of Artificial Neural Networks (ANNs), to predict the velocity in the test area of the Lundesokna River in Norway with diverse terrain conditions. Geographic data and flood velocity simulated based on the physical hydraulic model are used in the study for the pre-training, optimization, and testing of the MLP model. Our experiment indicates that the MLP model has the potential to predict flood velocity in diverse terrain conditions of the river with acceptable accuracy against simulated velocity results but with a significant decrease in training time and testing time. Meanwhile, we discuss the limitations for the improvement in future work.

To mitigate the greenhouse effect and environmental pollution caused by the consumption of fossil fuels, recent research has focused on developing renewable energy sources and new high-efficiency, environmentally friendly energy storage technologies. Among these, Li–CO2 batteries have shown great potential due to their high energy density, long discharge plateau, and environmental friendliness, offering a promising solution for achieving carbon neutrality while advancing energy storage devices. However, the slow kinetics of the CO2 reduction reaction and the accumulation of Li2CO3 discharge on the cathode surface lead to a significant reduction in space and active sites. This in turn results in high discharge overpotential, low energy efficiency, and low power density. This study elucidates the charge–discharge reaction mechanisms of lithium–carbon dioxide batteries and systematically analyzes their reaction products. It also summarizes the latest research advancements in cathode materials for these batteries. Furthermore, it proposes future directions and efforts for the development of Li–CO2 batteries.

As modern society continues to advance, the depletion of non-renewable energy sources (such as natural gas and petroleum) exacerbates environmental and energy issues. The development of green, environmentally friendly energy storage and conversion systems is imperative. The energy density of commercial lithium-ion batteries is approaching its theoretical limit, and even so, it struggles to meet the rapidly growing market demand. Lithium–oxygen batteries have garnered significant attention from researchers due to their exceptionally high theoretical energy density. However, challenges such as poor electrolyte stability, short cycle life, low discharge capacity, and high overpotential arise from the sluggish kinetics of the oxygen reduction reaction (ORR) during discharge and the oxygen evolution reaction (OER) during charging. This article elucidates the fundamental principles of lithium–oxygen batteries, analyzes the primary issues currently faced, and summarizes recent research advancements in air cathodes and anodes. Additionally, it proposes future directions and efforts for the development of lithium–air batteries.

Fe/N-doped carbon (Fe-NC) is an excellent base-metal catalyst for use in an electrocatalytic oxygen reduction reaction (ORR) with high activity. In this paper, graphite phase carbon nitride (g-C3N4) was first obtained from the pyrolyzing of melamine, and then different proportions of FeCl3 were separately doped into g-C3N4 to further prepare the Fe-NC catalyst. The Fe-NC catalyst was applied in an ORR reaction, and the results show that the Fe-NC catalyst doped with 0.5 mmol FeCl3 possesses exceptional electrocatalytic performance, with an onset potential of 0.96 V and a half-wave potential of 0.81 V, which approaches that of a Pt/C catalyst. Meanwhile, the Fe-NC catalyst displays high stability and methanol resistance. The results supply a new way to prepare efficient ORR electrocatalysts.

Cobalt-free manganese-based lithium-rich layered oxides (LLOs) have garnered research attention as prospective lithium-ion cathode materials owing to their large specific capacity and low price. However, their large-scale application is hindered by their low Coulombic efficiency, poor cycling performance, voltage attenuation, and structural phase transition. To address these issues, the LLO structure is modified via Ti doping at the manganese site herein. Ti-doped Li1.2Mn0.6−xTixNi0.2O2 (x = 0, 0.03, 0.05, 0.10, and 0.15) is prepared using the high-temperature solid-state method. The Ti-doped Li1.2Mn0.6Ni0.2O2 is calculated via first principles. The results show that Ti4+ doping improves the cycle stability and rate performance of Li1.2Mn0.6Ni0.2O2. Electrochemical test results show that the sample exhibits enhanced electrochemical performance when the Ti doping amount is 0.05. The discharge specific capacity at 0.1C is 210.4 mAh·g−1, which reaches 191.1 mAh·g−1 after 100 cycles, with a capacity retention rate of 90.7%. This study proves the feasibility of using cheap cobalt-free LLOs as cathode materials for LIBs and provides a novel system for exploiting low-cost and high-performance cathode materials.

The lack of tactile experience is a significant flaw in online product evaluation and purchasing, but visual information can be utilized to compensate for tactile deficits. This study constructed a conceptual model based on mental imagery theory, innovativeness theory, and the personal goals framework, to explore the mechanism of visual–tactile compensation on consumer purchase intention. We conducted an online experiment with 406 participants recruited from a community and online store in Southern China and tested the research hypotheses using structural equation modeling. The findings suggest that visually compensated tactile perceived diagnosticity promotes mental imagery and sensory similarity, which, in turn, affects purchase intention. Theoretically, this study enriches the current explanations of online haptics by explaining the mechanisms by which haptic demonstration videos influence consumers’ haptic evaluations and behavioral responses, as well as the moderating role of personal goals therein; practically, this study offers advice for retailers seeking to build or expand their tactile marketing strategies.

LiMn0.5Fe0.5PO4 (LMFP)@C and LMFP@LiAlO2@C nanorods are successfully synthesized by a solvothermal process followed by a calcination at H2/Ar atmosphere. The carbon coating and LiAlO2 coating does not change the morphology and particle size of LMFP, and all samples show nanorod morphology with 50–100 nm in width and 200–300 nm in length. The results show that LiAlO2 coating can offer rapid charge transfer channels with improved intercalation/de-intercalation kinetics of Li ions, which make an outstanding rate capability and cycling stability of as-synthesized LMFP@LiAlO2@C cathodes. As a result, LiAlO2 coating effectively improves the rate capability and cycling stability of LMFP cathode even at high discharge rates. Hence, LMFP@LiAlO2 (5 wt%)@C indicates an outstanding rate performance with a reversible discharge capacity of 137.6 and 113.2 mAh g−1 discharged at 0.05 C and 5 C rates, and the composite also shows a good cycle performance with an excellent capacity of 107 mAh g−1 and 86.4% capacity retention rate at 5 C rate after 100 cycles. Therefore, the LiAlO2 coating can be considered as an effective way to improve the electrochemical properties of LMFP. Keywords: LiAlO2, LiMn0.5Fe0.5PO4, Cycling stability, Cathode material, Li-ion battery

The adverse reaction of Black Hairy Tongue (BHT) caused by linezolid is rare. We reports a case of linezolid-induced BHT, and reviews relevant literatures at home and abroad. It aims to provide a safe and reasonable basis for clinical medication use. A 14-year-old adolescent with pneumonia caused by methicillin-resistant Staphylococcus aureus (MRSA) developed a rash and pruritus due to Vancomycin. Instead, the patient was given linezolid 600mg q12h in injection during hospitalization and in tablet after discharge. On the 14th day after injection and the second day after oral administration the patient showed BHT without other abnormal taste symptoms. But all the symptoms could be tolerated and he completed the therapy course of linezolid. Tongue symptoms completely disappeared on the 8th day after drug withdrawal. Based on the Karch and Lasagna evaluation methods and the cause-and-effect evaluation methods of the WHO collaborating center for international adverse drug reaction (ADR) monitoring, it is likely that this patient had a BHT caused by linezolid. The mean time of occurrence of BHT was 14.36 days, and the mean time of symptom disappearance was 23.43 days after drug administration. When linezolid is prescribed to patients, especially those with atopy, the patient's tongue should be closely observed and good oral hygiene is recommended.

To improve the RadCom system of performing both data transmission and radar-sensing functions, here a waveform design method for orthogonal frequency division multiplexing (OFDM) signal is proposed to solve the problem of high range sidelobe level in radar detection process while maintaining a reasonable channel capacity. The method only transmits data in the sub-channels with preferable channel quality. Then, calculate the weight of the chosen subcarriers through the convex optimisation method to traditionally minimise the peak-to-sidelobe (PSL) ratio. Simulation results prove that, compared with traditional range sidelobe suppression method, this algorithm is more suitable for RadCom system.

The identification of urban functional areas is essential for urban planning and sustainable development. Spatial grids are the basic units for the implementation of urban plans and management by cities or development zones. The emergence of internet “big data” provides new ideas for the identification of urban functional areas. Based on point of interest (POI) data from Baidu Maps, the Xicheng District of Beijing was divided into grids with side lengths of 200, 500, and 1000 m in this study. The kernel density method was used to analyze the spatial structure of POI data. Two indicators, that is, the frequency density and category ratio, were then used to identify single- and mixed-functional areas. The results show that (1) commercial and financial areas are concentrated in the city center and multiple business centers have not developed; (2) scenic areas account for the largest proportion of single-functional areas in the Xicheng District of Beijing, followed by education and training, residence, and party and government organizations areas; and (3) the 200 × 200 m and 500 × 500 m grids are the most suitable for the identification of single- and mixed-functional areas, respectively.

This paper addresses chaos suppression of the mechanical centrifugal flywheel governor system with output constraint and fully unknown parameters via adaptive dynamic surface control. To have a certain understanding of chaotic nature of the mechanical centrifugal flywheel governor system and subsequently design its controller, the useful tools like the phase diagrams and corresponding time histories are employed. By using tangent barrier Lyapunov function, a dynamic surface control scheme with neural network and tracking differentiator is developed to transform chaos oscillation into regular motion and the output constraint rule is not broken in whole process. Plugging second-order tracking differentiator into chaos controller tackles the “explosion of complexity” of backstepping and improves the accuracy in contrast with the first-order filter. Meanwhile, Chebyshev neural network with adaptive law whose input only depends on a subset of Chebyshev polynomials is derived to learn the behavior of unknown dynamics. The boundedness of all signals of the closed-loop system is verified in stability analysis. Finally, the results of numerical simulations illustrate effectiveness and exhibit the superior performance of the proposed scheme by comparing with the existing ADSC method.

This paper addresses chaos control of the micro-electro- mechanical resonator by using adaptive dynamic surface technology with extended state observer. To reveal the mechanism of the micro- electro-mechanical resonator, the phase diagrams and corresponding time histories are given to research the nonlinear dynamics and chaotic behavior, and Homoclinic and heteroclinic chaos which relate closely with the appearance of chaos are presented based on the potential function. To eliminate the effect of chaos, an adaptive dynamic surface control scheme with extended state observer is designed to convert random motion into regular motion without precise system model parameters and measured variables. Putting tracking differentiator into chaos controller solves the ‘explosion of complexity’ of backstepping and poor precision of the first-order filters. Meanwhile, to obtain high performance, a neural network with adaptive law is employed to approximate unknown nonlinear function in the process of controller design. The boundedness of all the signals of the closed-loop system is proved in theoretical analysis. Finally, numerical simulations are executed and extensive results illustrate effectiveness and robustness of the proposed scheme.

In this paper, chaos control is proposed for the output- constrained system with uncertain control gain and time delay and is applied to the brushless DC motor. Using the dynamic surface technology, the controller overcomes the repetitive differentiation of backstepping and boundedness hypothesis of pre-determined control gain by incorporating radial basis function neural network and adaptive technology. The tangent barrier Lyapunov function is employed for time-delay chaotic system to prevent constraint violation. It is proved that the proposed control approach can guarantee asymptotically stable in the sense of uniformly ultimate boundedness without constraint violation. Finally, the effectiveness of the proposed approach is demonstrated on the brushless DC motor example.

This paper is concerned with the problem of the nonlinear dynamic surface control (DSC) of chaos based on the minimum weights of RBF neural network for the permanent magnet synchronous motor system (PMSM) wherein the unknown parameters, disturbances, and chaos are presented. RBF neural network is used to approximate the nonlinearities and an adaptive law is employed to estimate unknown parameters. Then, a simple and effective controller is designed by introducing dynamic surface control technique on the basis of first-order filters. Asymptotically tracking stability in the sense of uniformly ultimate boundedness is achieved in a short time. Finally, the performance of the proposed controller is testified through simulation results.

This paper focuses on an adaptive dynamic surface control based on the Radial Basis Function Neural Network for a fourth-order permanent magnet synchronous motor system wherein the unknown parameters, disturbances, chaos, and uncertain time delays are presented. Neural Network systems are used to approximate the nonlinearities and an adaptive law is employed to estimate accurate parameters. Then, a simple and effective controller has been obtained by introducing dynamic surface control technique on the basis of first-order filters. Asymptotically tracking stability in the sense of uniformly ultimate boundedness is achieved in a short time. Finally, the performance of the proposed control has been illustrated through simulation results.