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The plasma emission spectrum and polycrystalline diamond growth within 4 inches was studied by using a microwave plasma chemical vapor deposition (MPCVD) equipment with a microwave frequency of 2.45 GHz and a power of 10 kW, a 4-inch free-standing diamond thick film was prepared, and the growth uniformity, crystal quality at different positions, room temperature thermal conductivity, ultraviolet-visible-near infrared spectral transmittance and dielectric properties of diamond film were characterized. The results show that the freestanding diamond films are prepared with good uniformity and crystallinity at 10 kW power with high concentration and chemical activity of carbon and hydrogen active particles within the plasma diameter of about 100 mm. The thermal conductivity at room temperature reaches more than 1894 W/m·K, the infrared transmittance reaches more than 70%, the dielectric constant is 5.5, and the dielectric loss is 5.9 × 10−5. This study provides a theoretical possibility for the preparation of large-area high-quality diamond films in low-power MPCVD equipment.

This article studies and develops an intelligent control emergency response system for power cable tunnels based on image recognition and ultra-wideband technology. The system aims to improve the safety monitoring and emergency response capabilities of cable tunnels by integrating real-time data transmission, multi-sensor data fusion, UWB (ultra-wideband) precise positioning, and AI (Artificial Intelligence) image recognition technology. The system has the ability to monitor data transmission and processing in real time and can quickly respond to emergency situations so as to effectively control sudden, hidden, and temporary security risks. In addition, the application of UWB technology provides precise positioning capability, which is crucial for ensuring personnel positioning in emergency response. AI image recognition technology can analyze surveillance video content, detect and handle potential construction hazards in a timely manner, and effectively prevent accidents from occurring. The application of this technology not only improves the efficiency of safety supervision but also enhances the safety guarantee ability of on-site operators.

This study comprehensively explores the application of ultra-wideband (UWB) technology in enhancing the safety and emergency protection of cable tunnel personnel. With the increase in electricity demand, traditional operation and maintenance methods of power grid cable tunnels are facing challenges, and there is an urgent need for more efficient and intelligent management methods. This article provides a detailed introduction to the principle, advantages, and application prospects of UWB positioning technology in cable tunnels, emphasizing its important role in high-precision positioning. UWB technology exhibits centimeter level precise positioning capability by emitting nanosecond narrow pulse signals and utilizing multipath propagation phenomena for ranging and positioning. This technology has multiple ranging methods, including one-way and two-way ranging based on time of arrival, as well as methods based on time difference. Each method has its own advantages and disadvantages, and it is suitable for different application scenarios. For example, one-way ranging is simple and direct but requires strict clock synchronization, while two-way ranging does not require high clock synchronization and has fewer communication times and low power consumption. In addition, this article also proposes an adaptive extended Kalman filtering algorithm for correcting ranging errors in non-line of sight environments, which significantly improves the stability and accuracy of the system in occluded environments. Through experimental verification, the algorithm effectively reduces the average error, further confirming the feasibility and effectiveness of UWB positioning technology in cable tunnel management. UWB technology has shown great potential in improving the safety of cable tunnels, providing an innovative solution for the construction and management of smart grids.

Abstract Multiferroic materials have ignited enormous interest owing to their co-existence of ferroelectricity and ferromagnetism, which hold substantial promise for advanced device applications. However, the size effect, dangling bonds, and interface effect in traditional multiferroics severely hinder their potential in nanoscale device applications. Recent theoretical and experimental studies have evidenced the possibility of realizing two-dimensional (2D) multiferroicity in van der Waals (vdW) layered CuCrP2S6. However, the incorporation of magnetic Cr ions in the ferroelectric framework leads to antiferroelectric and antiferromagnetic orderings, while macroscopic spontaneous polarization is always absent. Herein, we report the direct observation of robust out-of-plane ferroelectricity in 2D vdW CuCrP2S6 at room temperature with a comprehensive investigation. Modification of the ferroelectric polarization states in 2D CuCrP2S6 nanoflakes is experimentally demonstrated. Moreover, external electric field-induced polarization switching and hysteresis loops are obtained in CuCrP2S6 down to ~2.6 nm (4 layers). By using atomically resolved scanning transmission electron microscopy, we unveil the origin of the emerged room-temperature ferroelectricity in 2D CuCrP2S6. Our work can facilitate the development of multifunctional nanodevices and provide important insights into the nature of ferroelectric ordering of this 2D vdW material.

Abstract Surface amorphization provides electrocatalysts with more active sites and flexibility. However, there is still a lack of experimental observations and mechanistic explanations for the in situ amorphization process and its crucial role. Herein, we propose the concept that by in situ reconstructed amorphous surface, metal phosphorus trichalcogenides could intrinsically offer better catalytic performance for the alkaline hydrogen production. Trace Ru (0.81 wt.%) is doped into NiPS3 nanosheets for alkaline hydrogen production. Using in situ electrochemical transmission electron microscopy technique, we confirmed the amorphization process occurred on the edges of NiPS3 is critical for achieving superior activity. Comprehensive characterizations and theoretical calculations reveal Ru primarily stabilized at edges of NiPS3 through in situ formed amorphous layer containing bridging S2 2− species, which can effectively reduce the reaction energy barrier. This work emphasizes the critical role of in situ formed active layer and suggests its potential for optimizing catalytic activities of electrocatalysts.

Objectives: This retrospective cohort study aimed to analyze volumes of craniomaxillofacial bone and masticatory muscles of young adults with bilateral idiopathic condylar resorption. Methods: This was a retrospective cohort study of 84 adults with bilateral idiopathic condylar resorption (BCR) and 48 adults with normal temporal-mandibular joint (TMJ) matched for age and sex (mean age, 23.2 ± 3.6 years). The volumes of craniomaxillofacial bone and masticatory muscles, as well as intercondylar angle were measured. Unpaired t-tests and Pearson correlation tests were applied to analyze the data. Multivariable linear regression models were used to estimate the association between bilateral condylar volume and volumes of craniomaxillofacial bone and masticatory muscles adjusted for age, sex, and disc status. Results: Compared to the control group, the BCR group displayed significant decreased volumes of craniomaxillofacial bone (p

At present, there are three main methods for analyzing the causes of ship collision accidents: statistical analysis, accident causation models, and knowledge graphs. With the deepening of research, the analysis methods pay more attention to the objective correlation between various factors of the accident, and the analysis results obtained are more objective and accurate. On this basis, this paper proposes a method for analyzing the contribution degree of different causes and accident conduction paths in ship collision accidents based on the construction of the Ship Collision Accidents Event Graph (SCAEG). Firstly, the ontology is constructed based on the grounded theory. Secondly, events and relationships are extracted after fine-tuning the UIE model. Thirdly, the SCAEG is constructed after event coreference resolution. Finally, this research conducts the contribution degree analysis, accident conduction path analysis, and accident spatial distribution analysis based on SCAEG. The advantages of this method include the following: (i) it can construct a more complete and accurate ontology; (ii) adopting this approach can unify various information extraction tasks and achieve good results based on small sample annotation data; and (iii) using this method, we can conduct contribution degree analysis of different causes, accident conduction path analysis, and spatial distribution analysis. Experimental evidence demonstrates the effectiveness of this method. The analytical results obtained from the experiments can provide assistant decision-making for relevant departments to reduce the occurrence of ship collision accidents and improve maritime traffic safety.

Abstract The cooperative planning in intermodal transport networks can obtain the global optimal decision under the premise of ensuring the data privacy of each role in the cooperation and avoiding massive data transmission. For the control of container flow in intermodal transport networks, the distributed model predictive control (DMPC) method can effectively realize cooperative planning, but the convergence speed of the existing DMPC methods is slow. Therefore, this study attempts to construct faster DMPC methods for cooperative planning. The Jacobi proximal distributed model predictive control (JP‐DMPC) and dual consensus distributed model predictive control (DC‐DMPC) methods are constructed for container flow control based on two variants of alternating direction method of multipliers (ADMM). The simulation experiments prove that the convergence speed of JP‐DMPC and DC‐DMPC methods is higher than that of the state‐of‐the‐art method on the premise that the time cost and interaction data volume of each iteration do not change much, and the DC‐DMPC method improves planning speed particularly significantly. This study provides new methods for intermodal transport cooperative planning and has significance for the development of synchromodal transport.

Abstract Water is one of the most essential substances for life on Earth and plays a vital role in both natural and technological processes. Recently, there has been growing interest in studying the behavior of water molecules in confined spaces, particularly in low‐dimensional materials and structures. Regardless of whether it is in the form of gas, liquid, or solid, water can interact and form interfaces with many low‐dimensional structures. Given the current controversial understanding of two‐dimensional (2D) ice and the increasing interplay between water/ice and 2D materials such as graphene and transition‐metal dichalcogenides, we provide a brief overview of recent progresses on the interfaces of 2D ice and 2D van der Waals layered materials. This review highlights their potential contributions to the breakthroughs in tribology, membrane technology, nanofluidic, and nanodevice applications. Of particular interest is the recent discovery of ultrahigh lubricity between 2D ice and 2D layered materials, as well as the ability to modulate the surface adhesion between layers. These findings have the potential to enable new technological advances in both electronics and various industries. Meanwhile, this rapidly evolving field presents its own challenges, and we also discuss future directions for exploiting the interactions between 2D ice and 2D layered materials.

Abstract Electric‐field regulation of magnetic properties in perovskite manganites has attracted much attention for its potential in spintronics. For antiferromagnetic perovskite manganites, fewer studies are reported due to technological difficulties in probing their magnetic properties. Here, negative exchange bias (EB) is realized in epitaxial antiferromagnetic/ferromagnetic manganite bilayers with atomically flat interfaces. The low‐voltage pulse modulation of EB is demonstrated using the field‐effect device geometry with the ferroelectric copolymer, polyvinylidene fluoride with trifluoroethylene as a dielectric gating layer, antiferromagnetic La0.35Sr0.65MnO3 (AF‐LSMO) as pinning layer, and ferromagnetic La0.7Sr0.3MnO3 (FM‐LSMO) as conduction channel. Instead of using non‐volatile polarizations to control the EB, volatile polarizations in ferroelectric field effect transistors are suggested to be capable of modulating the EB. With high‐resolution electron microscopy and spectroscopy, the non‐volatile regulation of EB is attributed to the creation/annihilation of oxygen vacancies in the AF‐LSMO layer via low‐voltage pulses. This study reveals the effect of volatile electric polarizations in ferroelectric field effect devices and highlights the potential for low‐voltage pulse control of the physical properties in antiferromagnetic perovskite oxide insulators.

Recent advancements in transmission electron microscopy (TEM) have enabled the study of atomic structures of materials at unprecedented scales as small as tens of picometers (pm). However, accurately detecting atomic positions from TEM images remains a challenging task. Traditional Gaussian fitting and peak‐finding algorithms are effective under ideal conditions but perform poorly on images with strong background noise or contamination areas (shown as ultrabright or ultradark contrasts). Moreover, these traditional algorithms require parameter tuning for different magnifications. To overcome these challenges, AtomID‐Net is presented, a deep neural network model for atomic detection from multiscale low‐SNR experimental images of scanning TEM (scanning transmission electron microscopy (STEM)). The model is trained on real images, which allows the robust and efficient detection of atomic positions, even in the presence of background noise and contamination. The evaluation on a test set of 50 images with a resolution of 800 × 800 yields an average F1‐Score of 0.964, which demonstrates significant improvements over existing peak‐finding algorithms.

Surface wetting greatly impacts the performances of many photocatalysts in a water/humid‐involved medium. Carbon nitrides and its isotopes, as emerging metal‐free low‐cost photocatalysts for water splitting, usually require strong chemical or irradiation treatments to obtain highly hydrophilic surfaces, which can undermine their photocatalytic performances. Herein, an alternative method for the direct synthesis of superhydrophilic carbon nitride thin films (CN x , x ≈ 0.86–1.04) and graphitic carbon nitride powder (g‐C3N4) by using chemical vapor deposition is proposed. Less than 5° contact angle with water is accessible on both the surface of the as‐grown CN x thin films and the membranes made from the g‐C3N4 powder. It is found that the remarkable wetting property can be attributed to the spontaneous hydrophilic functionalization group (e.g., –OH, –NO x , = O) supplied by a constant multielemental air flow. The abundant CN triple bonds also promote needle‐shaped nanostructures on the 2D surfaces, which enhances their chemical wettability. Finally, the tremendous potential of this novel technique for direct synthesis of superhydrophilic carbon nitride in photocatalysis applications is demonstrated.

SrTiO3 (STO), a room-temperature paraelectric material in bulk form, has been a rich playground for emergent phenomena for decades. As an emerging material, great attention has been paid to freestanding 2D STO thin films. Recently, the room-temperature ferroelectricity has been unveiled in strained STO thin films; however, it remains an open question whether the strain-free freestanding 2D STO thin film is room-temperature ferroelectric or not. Here, we report the electric field-induced out-of-plane ferroelectric polarization in large-scale, freestanding, and strain-free 2D STO membranes at room temperature. High-resolution piezoresponse force microscopy measurements show that polarization in freestanding strain-free STO membranes can be switched by electric field and persist for an hour. The first-principles calculations suggest that the intrinsic defects such as oxygen vacancies could be linked to the observed spontaneous out-of-plane polarization in 2D STO membranes, which could be further enhanced by external electric field due to the induced symmetry breaking. Our work reports the unprecedented room-temperature ferroelectric polarization in strain-free freestanding 2D STO membranes, unlocking the great potential of freestanding 2D STO for the applications in novel electronic and logic-in-memory devices.

Abstract The traditional methods of introducing defects into alloy catalysts, such as dealloying, quenching, and doping, usually require complicated processes, rendering less controllability to the products and performances. Herein, a simple fabrication method for vacancy‐rich IrCo alloy nanoparticles supported on N‐doped carbon sheets (denoted as D‐IrCo/NC) is applied by post‐annealing the single atom (Ir and Co) dispersed precursors. The mobile single atoms and the coalescences of metallic clusters are directly observed via in situ transmission electron microscopy. Compared to the alloy catalysts obtained by direct calcination or other traditional methods, the D‐IrCo4.9/NC catalyst is enriched with vacancy defects and only demands an overpotential of 14 mV at j = 10 mA/cm2 for HER. Density functional theory (DFT) calculations reveal that the under‐coordinated Ir sites possess the lowest hydrogen adsorption energy. This novel preparation method is universal, and this work also provides a facile strategy to fabricate highly defective alloy catalysts evolved from single atom precursors.

Two-dimensional materials have well-defined atomic-scale structure, which has the potential to be tuned by processing. Here, substrate-induced straining during the growth of ReS2 causes the formation of martensite-like domain structures.

Abstract Phase patterning in polymorphic two‐dimensional (2D) materials offers diverse properties that extend beyond what their pristine structures can achieve. If precisely controllable, phase transitions can bring exciting new applications for nanometer‐scale devices and ultra‐large‐scale integrations. Here, the focused electron beam is capable of triggering the phase transition from the semiconducting T’’ phase to metallic T’ and T phases in 2D rhenium disulfide (ReS2) and rhenium diselenide (ReSe2) monolayers, rendering ultra‐precise phase patterning technique even in sub‐nanometer scale is found. Based on knock‐on effects and strain analysis, the phase transition mechanism on the created atomic vacancies and the introduced substantial in‐plane compressive strain in 2D layers are clarified. This in situ high‐resolution scanning transmission electron microscopy (STEM) and in situ electrical characterizations agree well with the density functional theory (DFT) calculation results for the atomic structures, electronic properties, and phase transition mechanisms. Grain boundary engineering and electrical contact engineering in 2D are thus developed based on this patterning technique. The patterning method exhibits great potential in ultra‐precise electron beam lithography as a scalable top‐down manufacturing method for future atomic‐scale devices.

Here, the authors use in situ transmission electron microscopy to measure the interface properties of electrical contacts with MoS2, ReS2, and graphene, and find that direct quantum tunnelling across van-der-Waals-bonded interfaces is more favourable than Fowler–Nordheim tunnelling across chemically bonded interfaces.

Abstract Tin (Sn)‐based perovskites with favorable optoelectronic properties and ideal bandgaps have emerged as promising alternatives to toxic lead (Pb)‐based perovskites for photovoltaic applications. However, it is challenging to obtain high‐quality Sn‐based perovskite films by solution process. Here, liquid‐exfoliated 2D transition‐metal dichalcogenides (i.e., MoS2, WS2, and WSe2) with smooth and defect‐free surfaces are applied as growth templates for spin‐coated FASnI3 perovskite films, leading to van der Waals epitaxial growth of perovskite grains with a growth orientation along (100). The authors find that WSe2 has better energy alignment with FASnI3 than MoS2 and WS2 and results in a cascade band structure in resultant perovskite solar cells (PSCs), which can facilitate hole extraction and suppress interfacial charge recombination in the devices. The WSe2‐modified PSCs show a power conversion efficiency up to 10.47%, which is among the highest efficiency of FASnI3‐based PSCs. The appealing solution phase epitaxial growth of FASnI3 perovskite on 2D WSe2 flakes is expected to find broad applications in optoelectronic devices.

Multilevel resistive switching in memristive devices is vital for applications in non-volatile memory and neuromorphic computing. In this study, we report on the multilevel resistive switching characteristics in SnSe/SrTiO3(STO) heterojunction-based memory devices with silver (Ag) and copper (Cu) top electrodes. The SnSe/STO-based memory devices present bipolar resistive switching (RS) with two orders of magnitude on/off ratio, which is reliable and stable. Moreover, multilevel state switching is achieved in the devices by sweeping voltage with current compliance to SET the device from high resistance state (HRS) to low resistance state (LRS) and RESET from LRS to HRS by voltage pulses without compliance current. With Ag and Cu top electrodes, respectively, eight and six levels of resistance switching were demonstrated in the SnSe/SrTiO3 heterostructures with a Pt bottom electrode. These results suggest that a SnSe/STO heterojunction-based memristor is promising for applications in neuromorphic computing as a synaptic device.

With the development of information technology, intermodal transport research pays more attention to dynamic optimization and multi-role cooperation. The core issue of this paper was to realize container routing with dynamic adjustment, real-time optimization, and multi-role cooperation characteristics in the intermodal transport network. This paper first introduces the Intermodal Transport Cooperation Protocol (ITCP) that describes the operation and analysis of intermodal transport systems with the concept of encapsulation and layering. Then, a new network flow control method was built based on Model Predictive Control (MPC) in the ITCP framework. The method takes real-time information from all ITCP layers as input and generates flow control decisions for containers. To evaluate the method’s effectiveness, a discrete event simulation experiment is applied. The results show that the proposed method outperforms the all-or-nothing method in scenarios with high freight volume, which means the method proposed in this paper can effectively balance the network transport load and reduce network operating costs. The research of this paper may throw some new light on intermodal transport research from the perspectives of digitization, multi-role cooperation, dynamic optimization, and system standardization.

Abstract In bulk crystals, the kinetics of dislocations is usually hindered by the twining boundaries (TB) or grain boundaries (GB), rendering the well‐known grain boundary strengthening effects. Nevertheless, here it is found that in 2D rhenium disulfide (ReS2), twinning is much easier than dislocation slip. Consequently, the highly mobile TBs or GBs are inversely pinned by the relatively immobile dislocations. Due to the strong in‐plane covalent bonding, the GBs in high‐symmetry 2D materials such as graphene which consists of defects are immobile at room temperature. In contrast, in monoclinic 2D ReS2 several types of GBs (including TBs) can be readily generated and driven by mechanical loading. A complete library of the GBs in 2D ReS2 is established by the (in situ) atomic‐scale transmission electron microscopy (TEM) characterizations and density functional theory (DFT) calculations. The twinning (shear) stresses for 2D ReS2 are estimated as low as 4–30 MPa, one or two orders of magnitude lower than the traditional bulk materials. Full elucidation on the GB structures and especially the intriguing GB kinetics in such anisotropic 2D materials are of fundamental importance to understand the structure–property relationships and develop strain‐tunable applications for 2D materials in future.

Abstract Owing to their high robustness and conductivity, 2D transition metal carbides and nitrides known as MXenes are considered as a promising material class for electrochemical catalysis, energy conversion, and storage applications. Nevertheless, conventional hazardous fluoride‐based synthesis routes and the intense intralayer bonding restrict the development of MXenes. Herein, a fluoride‐free, facile, and rapid method for synthesizing self‐assembled 1D architecture from an MXene‐based compound is reported. The MXene nanowire (NW) not only provides a robust connection to the flexible substrate but also effectively increases the electrochemically active surface area. The kinetics‐favorable structure yields a boosted performance for the hydrogen/oxygen evolution reaction and the intake of the zinc ion. The 1D NW based on MXene compound maintains high stability in a quite low overpotential of 236 mV for 24 h without detachment from the substrate and manifests an exceptional high‐power density of 420 W kg−1 over 150 cycles as a flexible aqueous zinc ion battery. This work paves a novel and non‐toxic synthesis method for the 1D nanofiber structure from MXene composition and demonstrates its multifunctional applications for energy conversion and storage.

Two dimensional monolayer transition metal ditellurides and their alloys are interesting but their growth has been difficult. Herein, Yun et al. demonstrate the use of sodium salts to convert transition metal disulfide to ditelluride and alloys in tellurium vapor at low temperature.

Tunnel settlement has a significant impact on property security and personal safety. Accurate tunnel-settlement predictions can quickly reveal problems that may be addressed to prevent accidents. However, each acquisition point in the tunnel is only monitored once daily for around two months. This paper presents a new method for predicting tunnel settlement via transfer learning. First, a source model is constructed and trained by deep learning, then parameter transfer is used to transfer the knowledge gained from the source model to the target model, which has a small dataset. Based on this, the training complexity and training time of the target model can be reduced. The proposed method was tested to predict tunnel settlement in the tunnel of Shanghai metro line 13 at Jinshajiang Road and proven to be effective. Artificial neural network and support vector machines were also tested for comparison. The results showed that the transfer-learning method provided the most accurate tunnel-settlement prediction.

How well the linear elastic fracture picture holds at small length scales and systems with reduced dimensionality remains an active area of inquiry. Here authors usein situ electron microscopy to study fracture in MoS2monolayers and report dislocation emission rates greater than expected accompanying crack propagation.

Accurate degradation state recognition of rolling bearing is critical to effective condition based on maintenance for improving reliability and safety. In this work, a new architecture is proposed to recognize the degradation state of the rolling bearing. Firstly, the time-domain features including RMS, kurtosis, skewness and RMSEE, and Mel-frequency cepstral coefficients features are extracted from bearing vibration signals, which are then used as the input of k-means algorithm. These unlabeled features are clustered by k-means in order to define the different categories of the bearing degradation state. In this way, the original vibration signals can be labeled. Then, the convolutional neural network recognition model is built, which takes the bearing vibration signals as input, and outputs the degradation state category. So, interference brought by human factors can be eliminated, and further, the bearing degradation can be grasped so as to make maintenance plan in time. The proposed method was tested by bearing run-to-failure dataset provided by the Center for Intelligent Maintenance System, and the result proved the feasibility and reliability of the methodology.

Grain boundaries can degrade the performance of electronic devices made from single atomic layers of transition metal dichalcogenides. Here, the authors combine transport measurements and transmission electron microscopy to find a correlation between field-effect mobility and grain misorientation angle.

Deep learning methods have been widely used in the field of intelligent fault diagnosis due to their powerful feature learning and classification capabilities. However, it is easy to overfit depth models because of the large number of parameters brought by the multilayer-structure. As a result, the methods with excellent performance under experimental conditions may severely degrade under noisy environment conditions, which are ubiquitous in practical industrial applications. In this paper, a novel method combining a one-dimensional (1-D) denoising convolutional autoencoder (DCAE) and a 1-D convolutional neural network (CNN) is proposed to address this problem, whereby the former is used for noise reduction of raw vibration signals and the latter for fault diagnosis using the de-noised signals. The DCAE model is trained with noisy input for denoising learning. In the CNN model, a global average pooling layer, instead of fully-connected layers, is applied as a classifier to reduce the number of parameters and the risk of overfitting. In addition, randomly corrupted signals are adopted as training samples to improve the anti-noise diagnosis ability. The proposed method is validated by bearing and gearbox datasets mixed with Gaussian noise. The experimental result shows that the proposed DCAE model is effective in denoising and almost causes no loss of input information, while the using of global average pooling and input-corrupt training improves the anti-noise ability of the CNN model. As a result, the method combined the DCAE model and the CNN model can realize high-accuracy diagnosis even under noisy environment.

EGb761, a standardized and well-defined product extract of Ginkgo biloba leaves, has beneficial effects on the treatment of multiple diseases, including diabetes and dyslipidemia. However, it is still unclear whether EGb761 can increase insulin sensitivity. The objectives of the present study are to evaluate the effects of EGb761 on insulin sensitivity in an obese and insulin-resistant mouse model, established through chronic feeding of C57BL/6J mice with a high-fat diet (HFD), and to explore potential mechanisms. Mice fed with HFD for 18 weeks (starting from 4 weeks of age) developed obesity, dyslipidemia (as indicated by biochemical measurements of blood glucose, triglyceride (TG), total cholesterol (TC), and free fatty acids (FFA)), and insulin resistance (as determined by the oral glucose tolerance test (OGTT) and the homeostasis model assessment of insulin resistance (HOMA-IR) index), compared to control mice fed with a standard laboratory chow. Oral treatment of the HFD-fed mice with EGb761, at low (100 mg/kg), medium (200 mg/kg), or high (400 mg/kg) doses, via oral gavage (once daily) for 8 weeks (starting from 26 weeks of age) dose-dependently enhanced glucose tolerance in OGTT, and decreased both the insulin levels (by 29%, 55%, and 70%, respectively), and the HOMA-IR index values (by 50%, 69%, and 80%, respectively). EGb761 treatment also ameliorated HFD-induced obesity, dyslipidemia, and liver injury, as indicated by decreases in body weight (by 4%, 11%, and 16%, respectively), blood TC levels (by 23%, 32%, and 37%, respectively), blood TG levels (by 17%, 23%, and 33%, respectively), blood FAA levels (by 35%, 38%, and 46%, respectively), and liver index (liver weight/body weight) values (by 12.8%, 25%, and 28%, respectively) in the low, medium, and high EGb761 dose groups, respectively. In further mechanism studies, EGb761 was found to protect hepatic insulin receptor β and insulin receptor substrate 1 from HFD-induced degradation, and to keep the AMP-activated protein kinase, which plays a crucial role in reducing lipotoxicity, from HFD-induced inactivation. We conclude that EGb761 can effectively reduce HFD-induced insulin resistance and ameliorate other symptoms of the metabolic syndrome.