Search

Although stem cell therapy holds promise for the treatment of spinal cord injury (SCI), its practical applications are limited by the low degree of neural differentiation. Electrical stimulation is one of the most effective ways to promote the differentiation of stem cells into neurons, but conventional wired electrical stimulation may cause secondary injuries, inflammation, pain, and infection. Here, based on the high conductivity of graphite and the electromagnetic induction effect, graphite nanosheets with neural stem cells (NSCs) are proposed as an electromagnetic cellularized patch to generate in situ wirelessly pulsed electric signals under a rotating magnetic field for regulating neuronal differentiation of NSCs to treat SCI. The strength and frequency of the induced voltage can be controlled by adjusting the rotation speed of the magnetic field. The generated pulsed electrical signals promote the differentiation of NSCs into functional mature neurons and increase the proportion of neurons from 12.5% to 33.7%. When implanted in the subarachnoid region of the injured spinal cord, the electromagnetic cellularized patch improves the behavioral performance of the hind limbs and the repair of spinal cord tissue in SCI mice. This work opens a new avenue for remote treatment of SCI and other nervous system diseases. [ABSTRACT FROM AUTHOR]

Ceramsite concrete is one of the most widely used lightweight concretes at present. Although mechanical properties of ceramsite concrete have been extensively discussed, its permeability characteristics are neglected in previous studies. Considering the importance of permeability resistance to concrete, the permeability grade and residual compressive strength after permeability of ceramsite concrete are analyzed in this study. The influence of ceramsite content and size on the permeability grade and residual strength of ceramsite concrete were investigated by the orthogonal experimental method. To further understand the above influence, an improved Bayesian framework for small sample data is also established to analyze the permeability grade and residual strength. Results show that the water–binder ratio and the content of 20–30 mm ceramsite aggregates are the most and least significant influencing factors affecting the permeability characteristics, respectively. The 5–10 mm and 10–20 mm ceramsite aggregates play secondary roles. Increasing 5–10 mm and 10–20 mm ceramsite aggregates is not helpful for improving the permeability resistance of ceramsite concrete. Compared with the orthogonal method, the proposed Bayesian framework is a useful tool for revealing the effects of various factors, which can cut the time cost and provide parameter visualization for the analysis process. Results show that the permeability resistance and residual strength of ceramsite concrete are improved significantly under optimal conditions. The permeability grade and residual strength are increased 200% and 80.3%, respectively. In addition, the residual strength may be more suitable for evaluating the permeability characteristics than the permeability grade. [ABSTRACT FROM AUTHOR]

The fundamentals of the design and operation of self-priming pumps, as indispensable equipment in industry, have been the focus of research in the field of fluid mechanics. This paper begins with a comprehensive background on self-priming pumps and gas-liquid two-phase flow, and it outlines recent advances in the field. Self-priming pumps within the gas-liquid two-phase flow state and the spatial and temporal evolution of the transient characteristics of self-priming pumps determine the self-priming pump self-absorption performance. Through mastery of the self-absorption mechanism, high-performance self-absorption pump products can be formed to provide theoretical support for the development of products. In current research, numerical simulation has become an important tool for analyzing and predicting the behavior of gas-liquid two-phase flow in self-priming pumps. This paper reviews existing numerical models of gas-liquid two-phase flow and categorizes them. Reviewing these models not only provides us with a comprehensive understanding of the existing research but also offers possible directions for future research. The complexity of gas–liquid interactions and their impact on pump performance is analyzed. Through these detailed discussions, we are able to identify the challenges in the simulation process and summarize what has been achieved. In order to further improve the accuracy and reliability of simulations, this paper introduces the latest simulation techniques and research methodologies, which provide new perspectives for a deeper understanding of gas-liquid two-phase flow. In addition, this paper investigates a variety of factors which affect the operating efficiency of self-priming pumps, including the design parameters, fluid properties, and operating conditions. Comprehensive consideration of these factors is crucial for optimizing pump performance. Finally, this paper summarizes the current research results and identifies the main findings and deficiencies. Based on this, the need to improve the accuracy of numerical simulations and to study the design parameters in depth to improve pump performance is emphasized. [ABSTRACT FROM AUTHOR]

In this paper, a reduced‐switch‐count two‐phase interleaved LLC converter with precise current balancing is proposed by using switching‐control‐capacitor (SCC) technology. Compared with the conventional SCC‐LLC converter, the proposed two‐phase SCC‐LLC converter reduces the switch count with the SCC circuits, and achieves excellent current sharing under all the tolerance conditions between the two‐phase resonant elements. In the proposed converter, the SCC circuit is only used in the first phase. The design of the SCC circuit parameters is analyzed to reduce the voltage stress of the SCC circuit and to ensure that two‐phase currents are balancing with large tolerance of resonant elements. A 2.16‐kW GaN‐based two‐phase interleaved SCC‐LLC converter prototype is established for data center application, and the experimental results show that the 98.1% peak efficiency, the 97.6% full load efficiency, and higher than 96% efficiency at the wide range from 3‐A to 40‐A load current are achieved. The precise current sharing lower than 1% is achieved by using a cheap ceramic capacitor with high tolerance, which validates the performance of the converter. [ABSTRACT FROM AUTHOR]

This study focused on the creation of Ag-MnO2 nanosheet composites by combining Ag and MnO2 nanosheets with the use of surfactant and organic solvents. The as-prepared adsorbents were analyzed for their oxidation properties in removing ethyl mercaptan through various characterization techniques including x-ray diffraction (XRD), transmission electron microscopy coupled with energy-dispersive x-ray spectroscopy, laser Raman spectroscopy, N2 adsorption–desorption isotherm analysis, and Fourier transformation infrared spectroscopy. The research findings revealed that Ag-MnO2-NS exhibits strong electromagnetic adsorption properties due to the robust interaction between silver and sulfur. The interaction between silver (Ag) and manganese dioxide (MnO2) was found to facilitate the oxidation of ethyl mercaptan (EM) into ethyl sulfonate and organosulfate. The combined effect of MnO2-NS and Ag was observed to enhance both the adsorption and catalytic oxidation of EM. This study delved into the adsorption and oxidation processes of EM using composite materials formed by combining silver nanoparticles and manganese dioxide nanosheets. The mechanism of oxidative adsorption was thoroughly analyzed through characterization, providing valuable insights for the efficient removal of EM. [ABSTRACT FROM AUTHOR]

Fly ash–slag-based alkali-activated materials have excellent mechanical performance and a low carbon footprint, and they have emerged as a promising alternative to Portland cement. Therefore, replacing traditional Portland cement with slag–desulfurization gypsum-based alkali-activated materials will help to make better use of the waste, protect the environment, and improve the materials' performance. In order to better understand it and thus better use it in engineering, it needs to be characterized for performance and compositional design. This study developed a novel framework for performance characterization and composition design by combining Categorical Gradient Boosting (CatBoost), simplicial homology global optimization (SHGO), and laboratory tests. The CatBoost characterization model was evaluated and discussed based on SHapley Additive exPlanations (SHAPs) and a partial dependence plot (PDP). Through the proposed framework, the optimal composition of the slag–desulfurization gypsum-based alkali-activated materials with the maximum flexural strength and compressive strength at 1, 3, and 7 days is Ca(OH)2: 3.1%, fly ash: 2.6%, DG: 0.53%, alkali: 4.3%, modulus: 1.18, and W/G: 0.49. Compared with the material composition obtained from the traditional experiment, the actual flexural strength and compressive strength at 1, 3, and 7 days increased by 26.67%, 6.45%, 9.64%, 41.89%, 9.77%, and 7.18%, respectively. In addition, the results of the optimal composition obtained by laboratory tests are very close to the predictions of the developed framework, which shows that CatBoost characterizes the performance well based on test data. The developed framework provides a reasonable, scientific, and helpful way to characterize the performance and determine the optimal composition for civil materials. [ABSTRACT FROM AUTHOR]

Transition bimetallic sulfides show significant promise for energy‐related applications because of their plentiful active sites and synergistic redox activity. However, limited pore size and low‐conductivity issues hinder their application. The structure of NiCo–S with rich sulfur vacancies is first predicted by density functional theory (DFT) calculations. Different sulfur vacancy concentrations are modeled by DFT calculations, and the results confirm that sulfur vacancies enhance the conductivity of the electrode material and are more beneficial for the adsorption of OH* species. It is verified by the differential charge density that the electric field formed on the surface of the electrode can lead to strong interfacial interactions by electron aggregation, which promotes electron/ion transfer kinetics. Furthermore, NiCo–S nanosheets are prepared on carbon cloth enriched with different concentrations of sulfur vacancies (denoted as NiCo‐Sv‐x, with x representing the concentration of sulfur vacancies) by sulfide etching NiCo‐MOF and annealing under H2/Ar atmosphere. The NiCo‐Sv‐x electrodes obtained are applied to the cathode of supercapacitors and the anode of the oxygen evolution reaction. Through combining experimental and theoretical analysis, the effect of vacancy defect engineering on the electrochemical performance of the electrode materials is further confirmed. [ABSTRACT FROM AUTHOR]

Change detection is an essential and widely utilized task in remote sensing that aims to detect and analyze changes occurring in the same geographical area over time, which has broad applications in urban development, agricultural surveys, and land cover monitoring. Detecting changes in remote sensing images is a complex challenge due to various factors, including variations in image quality, noise, registration errors, illumination changes, complex landscapes, and spatial heterogeneity. In recent years, deep learning has emerged as a powerful tool for feature extraction and addressing these challenges. Its versatility has resulted in its widespread adoption for numerous image-processing tasks. This paper presents a comprehensive survey of significant advancements in change detection for remote sensing images over the past decade. We first introduce some preliminary knowledge for the change detection task, such as problem definition, datasets, evaluation metrics, and transformer basics, as well as provide a detailed taxonomy of existing algorithms from three different perspectives: algorithm granularity, supervision modes, and frameworks in the Methodology section. This survey enables readers to gain systematic knowledge of change detection tasks from various angles. We then summarize the state-of-the-art performance on several dominant change detection datasets, providing insights into the strengths and limitations of existing algorithms. Based on our survey, some future research directions for change detection in remote sensing are well identified. This survey paper sheds some light the topic for the community and will inspire further research efforts in the change detection task. [ABSTRACT FROM AUTHOR]

Two-dimensional flexible materials with both piezoelectric and ferroelectric properties are regarded as the holy grail for realizing ultrathin functional electronic devices, which can be used in electronic flexible skin, high-performance storage, and medical diagnostic devices. However, the piezoelectric coefficients found in current two-dimensional (2D) piezoelectric-ferroelectric materials are comparatively limited in magnitude. Based on first-principles calculations, we demonstrate that 2D ThOTe monolayers exhibit not only remarkable flexibility in their mechanical properties (K/G > 1.75), but also exceptional piezoelectric and ferroelectric characteristics when subjected to strain engineering. Especially, they display an extraordinarily large piezoelectric constant, e11 = 13 181.19 pC m−1, and an impressively high piezoelectric coefficient of d11 = 354.95 pm V−1 at a tensile strain of 14%. Simultaneously, we discovered that the ThOTe monolayer possesses ferroelectric properties with an intrinsic electrode polarization, primarily aligned along the x-axis. The results introduce an innovative approach to explore ferroelectric materials within giant piezoelectric substances and provide valuable insights and references for the development of high-performance giant piezoelectric sensors, electronic devices and memories. [ABSTRACT FROM AUTHOR]

Visual navigation, characterized by its autonomous capabilities, cost effectiveness, and robust resistance to interference, serves as the foundation for vision-based autonomous landing systems. These systems rely heavily on runway instance segmentation, which accurately divides runway areas and provides precise information for unmanned aerial vehicle (UAV) navigation. However, current research primarily focuses on runway detection but lacks relevant runway instance segmentation datasets. To address this research gap, we created the Runway Landing Dataset (RLD), a benchmark dataset that focuses on runway instance segmentation mainly based on X-Plane. To overcome the challenges of large-scale changes and input image angle differences in runway instance segmentation tasks, we propose a vision-based autonomous landing segmentation network (VALNet) that uses band-pass filters, where a Context Enhancement Module (CEM) guides the model to learn adaptive "band" information through heatmaps, while an Orientation Adaptation Module (OAM) of a triple-channel architecture to fully utilize rotation information enhances the model's ability to capture input image rotation transformations. Extensive experiments on RLD demonstrate that the new method has significantly improved performance. The visualization results further confirm the effectiveness and interpretability of VALNet in the face of large-scale changes and angle differences. This research not only advances the development of runway instance segmentation but also highlights the potential application value of VALNet in vision-based autonomous landing systems. Additionally, RLD is publicly available. [ABSTRACT FROM AUTHOR]