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Nociceptors, crucial sensory receptors within biological systems, are essential for survival in diverse and potentially hazardous environments. Efforts to replicate nociceptors through advanced electronic devices, such as memristors and neuromorphic transistors, have achieved limited success, capturing basic nociceptive functions while more advanced characteristics like various forms of central sensitization and analgesic effect remain out of reach. Here, we introduce a vertical multigate, multichannel electrolyte-gated transistor (Vm-EGT), designed to mimic nociceptors. Utilizing the hybrid mechanism combining electric-double-layer (EDL) with ion intercalation/deintercalation in EGTs, our approach successfully replicates peripheral sensitization and desensitization characteristics of nociceptors. The intricate multigate and multichannel design of the Vm-EGT enables the emulation of more advanced nociceptive functionalities, including central sensitization and analgesic effect. Furthermore, we demonstrate that by exploiting the inherent current–voltage relationship, the Vm-EGT can simulate these advanced nociceptive features and seamlessly transition between them. Integrating a Vm-EGT with a thermistor and a heating plate, we have developed an artificial thermal nociceptor that closely mirrors the sensory attributes of its biological counterpart. Our approach significantly advances the emulation of nociceptors, providing a basis for the development of sophisticated artificial sensory systems and intelligent robotics.

In recent years, multifarious new materials have been developed for environmental governance. Thereinto, metal organic framework (MOF)-based catalysts have been widely employed for heterogeneous catalysis because of their high porosity to confine noble metal particles faraway from aggregation. However, the potential reactions between residual species from the material synthesis process and target pollutants, which could form highly toxic byproducts, are often neglected. Herein, we took the widely used Zr-MOF, UiO-66, with highly thermal stability supported Pd catalysts as the example to investigate how the residual species in catalysts are involved in aromatic volatile organic compounds (VOCs) degradation reaction. The results showed that residual Cl species originated from the ZrCl4metal precursor participated in the VOC degradation reaction, leading to the production of various chlorine-containing byproducts, even the hypertoxicity dioxin precursor, dichlorobenzene. Meanwhile, the chlorination mechanism for the formation of chlorine-containing byproducts was revealed by density functional theory calculation. Furthermore, the highly efficient residual Cl removal approaches are proposed. Importantly, the migration and transformation of residual Cl during the degradation of five benzene series VOCs are comprehensively studied and elucidated. We anticipate that these findings will raise alarm about the neglected issue of residual species in MOF-based catalysts for heterogeneous catalysis, especially environmentally friendly catalysis.

In an effort to transcend the limitations of conventional eddy current sensing systems that have thus far been confined to demonstrating its capabilities within a constant sensing strategy to passively obtain digital datasets, this article proposes a prototype of an intelligent eddy current sensing system based on the neuromorphic-induced electrodynamics model rooted in the physical domain. Within this system, a physical electromagnetic (PEM) neural network has developed by employing transmitters and receivers as its physical neural entities, enabling the sensing system to directly and intelligently process the physical properties in the real physical world. Furthermore, a physics-informed reinforcement learning (RL) framework has been harnessed to dynamically manipulate the weights of the PEM network, ensuring the adaptive maintenance of optimal sensing conditions in evolving environments. Through this approach, the proposed sensing system implemented a self-decision-making mechanism and generated a self-curated optimal physical dataset and corresponding labels. Through a specific application in nondestructive material testing, the adaptability of the proposed system in achieving optimal sensing conditions in varying environments is validated. This article lays the potential paradigm shift that allows artificial intelligence (AI) to extend its scope from the digital domain to the real physical domain and may open up more applications, such as smart sensors, data generation, and online inference tasks.

This article proposes a universal front-end identification method for mutual inductance and load based on the eigenstate mechanism, which is suitable for magnetic coupling wireless power transfer (MC-WPT) systems with arbitrary topologies. First, the general real eigenstate parameter criterion for arbitrary topology MC-WPT systems is established. Then, the general analytical expressions of mutual inductance and load under the real eigenstate are derived. Based on the real eigenstate impedance characteristics, a front-end parameter identification method and a process are presented. The proposed method is universally applicable to any topological structure by simply inputting the corresponding impedance parameters. Meanwhile, the constructed mutual inductance and load analytical expressions are decoupled, eliminating the need for iterative processes and concerns about iteration count and convergence precision. This results in fast computation speed and high accuracy. In experiments, the identification accuracy for mutual inductance and load is above 93.95% and 98.22%, respectively. Furthermore, the method does not require any additional identification circuits and combines the advantages of the eigenstate mechanism, such as high energy efficiency and strong antioffset capability. Additionally, the established general real eigenstate parameter criterion can effectively facilitate the design of parity-time symmetry mechanisms in systems with various topologies.

Insect defensins are a large family of antimicrobial peptides primarily active against Gram-positive bacteria. Here, we explore their hidden anti-Gram-negative bacterial potential via a nature-guided strategy inspired by natural deletion variants of Drosophiladefensins. Referring to these variants, we deleted the equivalent region of an insect defensin with the first cysteine-containing N-terminus, and the last three cysteine-containing C-terminal regions remained. This 15-mer peptide exhibits low solubility and specifically targets Gram-positive bacteria. Further deletion of alanine-9 remarkably improves its solubility, unmasks its hidden anti-Gram-negative bacterial activity, and alters its states in different environments. Intriguingly, compared with the oxidized form, the 14-mer reduced peptide shows increased activity on Gram-positive and Gram-negative bacteria through a membrane-disruptive mechanism. The broad-spectrum activity and tolerance to high-salt environments and human serum, together with no toxicity to mammalian or human cells, make it a promising candidate for the design of new peptide antibiotics against Gram-negative bacterial infections.

Depression, as a common mental illness, has become a significant public health issue, and the recurrence rate for patients with depression who have been treated is relatively high. In this study, a mental health monitoring system based on wearable sensing wristbands with sensors for voice, activity, and heart rate has been developed. Using this system, we perform a therapeutic monitoring study for hospitalized patients with depression and healthy controls to investigate multimodal changes before, during, and after a course of treatment. The obtained results demonstrate that there are significant changes in multimodal features such as audio short-time energy and angular velocity shape skewness with the remission of depressive symptoms. According to Mikels’ emotion wheel, a day’s data for subjects is defined as three types of emotional units and the emotional state of each emotional unit is recognized as positive or negative emotions. With this, emotion-sensing-graphs guided by Mikels’ emotion wheel theory are constructed. The analysis of emotion-sensing-graphs reveals that the same emotions are more closely linked to each other and the average degree and proportion of positive emotion nodes after a course of treatment have increased significantly. Finally, an emotion-sensing-graph graph convolutional network (ESG-GCN) model fused three types of emotion-sensing-graphs with emotion labels has been developed to assess the levels of depression, thereby monitoring the changes in depressive symptoms. Compared with classical machine learning models, the accuracy, F1 score, and recall rate of the model perform best and the model achieves a verification accuracy of 0.83.

In-sensor computing, which integrates sensing, memory and processing functions, has shown substantial potential in artificial vision systems. However, large-scale monolithic integration of in-sensor computing based on emerging devices with complementary metal–oxide–semiconductor (CMOS) circuits remains challenging, lacking functional demonstrations at the hardware level. Here we report a fully integrated 1-kb array with 128 × 8 one-transistor one-optoelectronic memristor (OEM) cells and silicon CMOS circuits, which features configurable multi-mode functionality encompassing three different modes of electronic memristor, dynamic OEM and non-volatile OEM (NV-OEM). These modes are configured by modulating the charge density within the oxygen vacancies via synergistic optical and electrical operations, as confirmed by differential phase-contrast scanning transmission electron microscopy. Using this OEM system, three visual processing tasks are demonstrated: image sensory pre-processing with a recognition accuracy enhanced from 85.7% to 96.1% by the NV-OEM mode, more advanced object tracking with 96.1% accuracy using both dynamic OEM and NV-OEM modes and human motion recognition with a fully OEM-based in-sensor reservoir computing system achieving 91.2% accuracy. A system-level benchmark further shows that it consumes over 20 times less energy than graphics processing units. By monolithically integrating the multi-functional OEMs with Si CMOS, this work provides a cost-effective platform for diverse in-sensor computing applications.

As an effective method for detecting inner defects in composite materials, optical pulsed thermography (OPT) nondestructive testing (NDT) is widely used in the aircraft industry. Due to the complex structure of the large irregular composite materials, traditional manual inspection leads to uneven heat conduction, low defect detection rate, and lack of automation. Studying the physical properties of heat conduction found that surface thermal uniformity is particularly sensitive to defect positioning errors as well as resolution. This article proposes an adaptive defect detection method based on thermal perception guided Robot-sensing-feedback controlling within the heat flux density isobaric surface (HFDIS) projection. In particular, it is constructed by the physical projection of the OPT as embedded with the 3-D model of the sample. HFDIS can be simultaneously used to reconstruct the 3-D thermography of the irregular sample, introduce closed-loop constraints, globally optimize the heat flux density uniformity loss function, and control the detection pose of the robotic arm. Thus, it improves heat conduction uniformity and defect detection accuracy and efficiency. Both simulation and experimental verification were conducted on multiple types of heterogeneous and special-shaped samples provided by the aircraft company, and the effectiveness and scientific validity of the detection method were rigorously evaluated.

This article introduces a new imaging approach to nondestructive defect detection by combining visual testing (VT) and infrared thermal testing (IRT) in a multispectral vision sensing fusion system. The goal is to overcome the hampering challenges faced by traditional imaging methods, including complex environments, irregular samples, various defect types, and the need for efficient detection. The proposed system simultaneously detects and classifies surface and subsurface defects, addressing issues, such as false detection due to changes in surface emissivity in IRT and the inability to detect subsurface defects in VT. A novel multispectral fusion defect detection framework is proposed, employing coarse-to-fine multispectral registration for accurate alignment of infrared and visible images with different resolutions and fields of view. Domain adaptation unifies the feature domains of infrared and visible images by replacing the phase components in the frequency domain. The framework utilizes the complementary information from infrared and visible modalities to enhance detection accuracy and robustness. Experimental validation is conducted on different specimens, confirming the effectiveness of the proposed framework in detecting and generalizing to various shapes and materials. Overall, this article presents a novel imaging system that combines VT and IRT, offering improved detection capabilities in complex environments and diverse defect scenarios. The demo code is available at: https://github.com/ljcuestc/YoloMultispectralFusion-Coarse-to-fine-Registration.gi.

ObjectivePrevious studies indicate that eosinophils are recruited into the allograft following orthotopic liver transplantation and protect from ischaemia reperfusion (IR) injury. In the current studies, we aim to explore whether their protective function could outlast during liver repair.DesignEosinophil-deficient mice and adoptive transfer of bone marrow-derived eosinophils (bmEos) were employed to investigate the effects of eosinophils on tissue repair and regeneration after hepatic IR injury. Aside from exogenous cytokine or neutralising antibody treatments, mechanistic studies made use of a panel of mouse models of eosinophil-specific IL-4/IL-13-deletion, cell-specific IL-4rα-deletion in liver macrophages and hepatocytes and macrophage-specific deletion of heparin-binding epidermal growth factor-like growth factor (hb-egf).ResultWe observed that eosinophils persisted over a week following hepatic IR injury. Their peak accumulation coincided with that of hepatocyte proliferation. Functional studies showed that eosinophil deficiency was associated with a dramatic delay in liver repair, which was normalised by the adoptive transfer of bmEos. Mechanistic studies demonstrated that eosinophil-derived IL-4, but not IL-13, was critically involved in the reparative function of these cells. The data further revealed a selective role of macrophage-dependent IL-4 signalling in liver regeneration. Eosinophil-derived IL-4 stimulated macrophages to produce HB-EGF. Moreover, macrophage-specific hb-egf deletion impaired hepatocyte regeneration after IR injury.ConclusionTogether, these studies uncovered an indispensable role of eosinophils in liver repair after acute injury and identified a novel crosstalk between eosinophils and macrophages through the IL-4/HB-EGF axis.

Resistive random access memory (RRAM) has been extensively studied for high-density memory and energy-efficient computing-in-memory (CIM) applications. In this work, for the first time, we present a fully integrated 3-D stackable 1-kb one-CNTFET-one-RRAM (1T1R) array with carbon nanotube (CNT) CMOS peripheral circuits. The 1T1R cells were fabricated with 1024 CNT NFETs and Ta2O5-based multibit RRAMs, while the peripheral circuits consisted of 747 CNT PFETs and 875 NFETs for the word line (WL) 7:128 decoder and 128 drivers. The entire array was fabricated using a low-temperature ( $\le 300~^{\circ} \text{C}$ ) process, enabling multiple layers of CNTFET/RRAM arrays to be vertically stacked in the backend-of-the-line (BEOL) to boost the integration density and chip functionality. Furthermore, this 1T1R digital memory array was then used as a BEOL buffer macro and monolithically 3-D (M3D) integrated with another 128-kb HfO2-based analog RRAM array and Si CMOS logic to accelerate CIM. The fabricated M3D-CIM chip consisted of three functional layers, whose structural integrity and proper function was validated by extensive structural analysis and electrical measurements. To highlight the advantages of this M3D-CIM architecture, typical neural networks, such as multilayer perceptron (MLP) and ResNET32, were implemented, achieving a GPU-equivalent classification accuracy of up to 96.5% in image classification tasks while consuming $39\times $ less energy. Therefore, this work demonstrates the tremendous potential of the CNT/RRAM-based M3D-CIM architecture for various artificial intelligence (AI) applications.

Computing-in-memory (CIM) based on analog resistive random access memory (RRAM) emerges as an energy-efficient technology for edge artificial intelligence (AI), where a large amount of ON-chip data buffer is needed to implement complex neural networks. In this work, we report a novel InGaZnOx (IGZO)/carbon nanotube (CNT) hybrid-polarity 2T0C DRAM as a backend-of-the-line (BEOL) compatible buffer, which is a monolithic 3-D (M3D) integrated with HfO2-based analog RRAM array and Si CMOS logic to demonstrate a M3D-BRIC chip. The structural integrity and proper function of each layer are systematically verified. In particular, by incorporating n-type ultralow leakage IGZO field-effect transistor (FET) for write transistor and p-type high-current CNT-FET for read, this unique hybrid-polarity 2T0C design achieves a decent retention and desirably large read currents. It also helps enhance the effective sensing window and, more importantly, resolve the charge injection issue via counteractive coupling. To demonstrate the computational advantage of M3D-BRIC architecture, a typical high-resolution (Hi-Res) video processing task is further implemented using the YOLOv3 network for object detection. The benchmark shows that the M3D-BRIC chip with BEOL 2T0C DRAM could achieve a $48.25\times $ higher processing capability compared to its 2-D counterpart.

Cardiovascular disease is known as the number one cause of death worldwide. Ultrasound imaging is widely used to assess and predict the risk of cardiovascular disease. Extensive research on robotic ultrasound systems has addressed the problems of over-reliance on physician experience in the application of ultrasound, unequal distribution of medical resources, and mutual exposure of patients and physicians to infections. The human carotid artery requires more standardization, safety, and intelligence in applying robotic ultrasound systems due to its sensitive location and inconspicuous skin features. This article introduces an innovative robot-assisted system for autonomous ultrasound scanning of the carotid artery. The system uses a structured two-stage approach, encompassing prescanning and scanning, to enhance the precision and efficiency of the procedure. It uses advanced image servo control for intelligent scanning path determination, significantly improving the quality and speed of scans. Moreover, the system’s capability to alternate between transverse and longitudinal scanning modes allows it to accommodate the unique anatomical differences in individual patients. Experimental results on real human subjects showed a success rate of 86.11% for scanning the desired carotid artery ultrasound images, which is promising for clinical applications.

Lift-off has a significant impact on defect discrimination and qualification. Estimating lift-off and evaluating defects is a critical issue for eddy current testing (ECT). In this article, a novel sensor that separates lift-off and defects is proposed. This uses common- and differential-mode coils with a dual signal conditioning circuit. Specifically, it comprises two hollow rectangular excitation coils located on the first and third layers of the printed circuit board (PCB), serving as the two arms of an ac bridge circuit. The output from the bridge signal is used for lift-off estimation since the coaxial configuration and small coil fill ratio make it specifically sensitive to lift-off variation. The other pair of rectangular receiver coils are situated on the second and third layers and placed in the middle of the hollow transmitter. This forms a transformer with excitation coils and is only sensitive to defects. The signals from these two complementary outputs allow lift-off to be used as auxiliary information for defect discrimination. Simulations and experiments were carried out to validate the method. This proves that a combination of coil configurations with different signal conditioning techniques can be employed for separating multiple parameters.

The tensor nuclear norm (TNN), defined as the sum of nuclear norms of frontal slices of the tensor in a frequency domain, has been found useful in solving low-rank tensor recovery problems. Existing TNN-based methods use either fixed or data-independent transformations, which may not be the optimal choices for the given tensors. As the consequence, these methods cannot exploit the potential low-rank structure of tensor data adaptively. In this article, we propose a framework called self-adaptive learnable transform (SALT) to learn a transformation matrix from the given tensor. Specifically, SALT aims to learn a lossless transformation that induces a lower average-rank tensor, where the Schatten- $p$ quasi-norm is used as the rank proxy. Then, because SALT is less sensitive to the orientation, we generalize SALT to other dimensions of tensor (SALTS), namely, learning three self-adaptive transformation matrices simultaneously from given tensor. SALTS is able to adaptively exploit the potential low-rank structures in all directions. We provide a unified optimization framework based on alternating direction multiplier method for SALTS model and theoretically prove the weak convergence property of the proposed algorithm. Experimental results in hyperspectral image (HSI), color video, magnetic resonance imaging (MRI), and COIL-20 datasets show that SALTS is much more accurate in tensor completion than existing methods. The demo code can be found at https://faculty.uestc.edu.cn/gaobin/zh_CN/lwcg/153392/list/index.htm.

Automated optical inspection (AOI) is a significant process in printed circuit board assembly (PCBA) production lines which aims to detect tiny defects in PCBAs. Existing AOI equipment has several deficiencies including low throughput, large computation cost, high latency, and poor flexibility, which limits the efficiency of online PCBA inspection. In this paper, a novel PCBA defect detection method based on a lightweight deep convolution neural network is proposed. In this method, the semantic segmentation model is combined with a rule-based defect recognition algorithm to build up a defect detection framework. To improve the performance of the model, extensive real PCBA images are collected from production lines as datasets. Some optimization methods have been applied in the model according to production demand and enable integration in lightweight computing devices. Experiment results show that the production line using our method realizes a throughput more than three times higher than traditional methods. Our method can be integrated into a lightweight inference system and promote the flexibility of AOI. The proposed method builds up a general paradigm and excellent example for model design and optimization oriented towards industrial requirements.

Electronic correlation effects are manifested in quantum materials when either the on-site Coulomb repulsion is large or the electron kinetic energy is small. The former is the dominant effect in cuprate superconductors and heavy-fermion systems whereas it is the latter in twisted bilayer graphene and geometrically frustrated metals. However, the simultaneous cooperation of both effects in the same quantum material remains rare. The design aim is to produce correlated topological flat bands pinned at the Fermi level. Here, we observe a flat band at the Fermi level in a 3dpyrochlore metal CuV2S4. Our angle-resolved photoemission spectroscopy data reveal that destructive quantum interference associated with the V pyrochlore sublattice and further renormalization to the Fermi level by electron interactions induce this flat band. Consequently, we discover transport signatures that evidence a deviation from Fermi liquid behaviour as well as an enhanced Sommerfeld coefficient. Our work illustrates the combined cooperation of local Coulomb interactions and geometric frustration in a pyrochlore lattice system to induce correlated topology by constructing and pinning correlated flat bands near the Fermi level.

The relaxation effect in analog resistive random access memory (RRAM) poses a significant challenge in the implementation of neuromorphic systems, as it leads to a loss of accuracy in computing. However, due to the inherent interdependence of various fluctuations and underlying mechanisms, the relaxation effect is still challenging. In this study, we have developed a high-quality adaptive relaxation signal analysis method by analyzing the read current during the relaxation process. This method enables the identification of all conductivity demarcation points in relaxation and categorizes them into different types of fluctuations. Importantly, we have investigated distinct fluctuations in relaxation and their corresponding mechanisms, which is a comprehensive analysis of fluctuations in the relaxation effect. We propose an optimization strategy based on our understanding of these mechanisms: increasing the pulsewidth. This strategy aims to mitigate relaxation effects and reduce the relative accuracy loss of convolutional neural networks (CNNs).

Eddy current (EC) detection technique has been applied for the inspection of small-diameter pipelines whereas it retains the limitation in detecting defects on the outer wall of the pipeline. This article proposes new instrumentation of cross electromagnetic EC sensing for internal pipeline inspection. The technique integrates the principles of remote field EC (RFEC) and near field EC and involves exciting two types of magnetic fields inside the pipeline. The first magnetic field is oriented perpendicular to the pipe wall, which allows for the detection of inner wall defects. The second magnetic field propagates along the pipeline direction, enabling the detection of outer wall defects. By combining these two magnetic fields, the proposed technique enables simultaneous detection of both inner and outer wall defects in small-diameter buried pipelines. A detection platform for an experimental laboratory system has been built, and an internally integrated system developed for pipelines with an 8-in diameter. By conducting inspections on various pipelines, defects on both the inner and outer walls were successfully detected. Results have validated the reliability and efficiency of the proposed detector.

In this study, we propose a new post processing algorithm, using a stable low-rank decomposed pseudo restored heat flux based on the ensemble variational Bayes tensor factorization (EVBTF-RPHF) algorithm for performing periodic square wave thermographic nondestructive testing (thermographic NDT). Previous studies have shown that both RPHF and EVBTF can separately improve the detectability of thermography by enhancing some defect features. However, both methods are limited by their particularly constraints: RPHF are heavily degraded by noises and missing data due to the assumptions under which the physical models are derived while efficiency of EVBT reduces when the lateral heat diffusion weights out. By embedding RPHF into the stable low-rank decomposition EVBTF, the proposed algorithm allows to improve the detectability of defects in thermographic NDT using a periodic heat flux with low-rank spatial distribution. The study verifies the capacity of the proposed method by theoretical analysis. Then, experiments were conducted on a carbon fiber composite panel with foreign inserts buried up to 5 mm deep. The sampled data are processed by the proposed method. The results are compared with existing methods such as phase-locked RPHF and EVBTF. The experimental results demonstrated that defects with normalized diameter-to-depth ratios as small as 0.9, barely detected with other available techniques, can reliably be detected by EVBTF-RPHF. The signal to noise ratio and the contrast are used as figure of merit to quantitatively compare the capacity of the proposed method with existing methods. However, the computation efficiency of the proposed algorithms needs further improvement.

Nonvolatile SRAM (nvSRAM), which integrates the nonvolatile elements with SRAM using a direct bit-to-bit connection has raised much attention in the past few years, owing to its fast parallel data transfer and fast power-on/off speed. However, few nvSRAM macros have been silicon verified to be enacted through the power-failure event. On the other hand, the capacity of fabricated nvSRAM macro is small (~ Kbit) to date, inhibiting its practical application. This study presents a novel ReRAM-based nvSRAM bitcell with improved reliability and scalability. A 64KB nvSRAM macro was designed and integrated into a 32-bit microcontroller unit (MCU). The chip was fabricated using HfOx-based BEOL ReRAM and a 130nm CMOS technology. To pursue fast storage, a write-without-verify scheme is adopted to program ReRAM, measurement results show that the raw bit error rate between the power outages is < 0.1% for the full macro under such constraint. Cryptography and machine learning applications are successfully performed on the MCU system. For the first time, with the help of correction techniques, we achieved an error-free nvSRAM macro that is reliable enough to store/restore programs and demonstrated a real-time robotic control system empowered by the nvSRAM. The proposed nvSRAM macro has the largest capacity to date.

Since their implementation in China’s tertiary education system two decades ago, EMI programmes have been reported largely less successful and more problematic than envisioned. Although portrayed as killing two birds with one stone, whereby both subject content learning and English language proficiency can be achieved in the same classroom, EMI has in reality been revealed to present a series of thorny problems, from teaching quality to learning achievements, from teachers’ insufficient language proficiency to students’ unsatisfactory academic outcomes. This paper addresses one of the critical issues in EMI implementation: what teachers need in order to bring about successful language learning as well as adequate subject content learning. Data sources include a questionnaire collected from different disciplinary programmes across universities in China (n= 158), and interviews of nine lecturers from both key and non-key universities. The results suggest that the needs of these EMI lecturers cover a variety of areas, including institutional support as well as needs for professional and pedagogical training. Our findings indicate that there are similarities and differences in teacher needs between key and non-key universities with regard to institutional support and training programmes. The findings suggest that epistemic environments as well as goal-oriented pedagogical activities and adequate discourse strategies are necessary to enhance the learning experience of the students, facilitate the integration of content and language learning, and empower teachers and students to identify the best classroom practices. The needs analysis is instrumental for developing both in-service and pre-service training programmes for EMI implementation in higher education and crucial if the goal of ‘killing of two birds with one stone’ is to be achieved.

ABSTRACTObjectivesTo explore the relationship between serum uric acid (UA) and high-density lipoprotein cholesterol (HDL-C) ratio (UHR) and metabolic syndrome (MetS) in nondiabetic individuals.MethodsA total of 15,760 nondiabetic participants were screened from the China National Diabetes and Metabolic Disorders Study. Pearson correlation was used to determine the correlation between the components of MetS and UHR, HDL-C, and UA. Receiver operating characteristic curves were used to evaluate the ability of UHR, HDL-C, and UA to identify MetS in the nondiabetic population.ResultsA total of 6,386 men and 9,374 women were enrolled in this study. There were 1,480 (23.2%) men and 1,828 (19.5%) women with MetS. UHR significantly correlated with the components of MetS in men and women, especially with waist circumference  and triglyceride. In men, although HDL-C showed a higher specificity index, UHR presented higher sensitivity index and area under the curve (AUC) than HDL-C (P = 0.0001) and UA (P < 0.0001), with AUC (95% CI) of 0.762 (0.752–0.773). Higher AUCs of UHR relative to HDL-C and UA were also observed in the age groups <40 and 40–59 years. There was no significant difference in AUC between UHR and HDL-C in the age group ≥60 years (P = 0.370). However, similar results were not observed in women.ConclusionUHR significantly correlated with the components of MetS and could serve as a novel and reliable marker for identifying the population at a high risk of MetS in nondiabetic men, especially in younger adults.

Both ammonium sulfite slurry (ASS) from ammonia-based desulfurization and lignite are waste materials with low value. In this work, an innovative method was developed by applying ASS in lignite activation to produce water-soluble humic substances (WHSs) with a high bioactivity and economic value. The optimal activation method was to mix lignite and ASS at a 4:1-liquid–solid ratio by vortex blender and then oscillate it for 30 min at 25 °C. Compared with that of the unactivated lignite (UAL), the yield of WHSs from activated lignite (AL) increased by 42.72%. WHSs from AL consisted of a large number of aliphatic carbons with low molecular weight and functional groups such as amides, amines, sulfonic acid groups, C–O, and so forth. Moreover, WHSs from AL at lower concentrations (2 mg/L) has a more obvious root-elongation-promoting effect than WHSs from UAL (10 mg/L). Activation experiment with the lignite-related model compounds revealed that ASS caused the breakage of Caliph–O, Caliph–Caliph, and Carom–Caliphlinkages between aromatic rings. These findings provide a theoretical basis for the development of green and sustainable technologies for the beneficial reuse of ASS and lignite in agriculture.

In this article, we review the development of emerging memory-based neuromorphic computing. First, we discuss the motivation and advantages of this approach. We then summarize the mechanisms and electrical behaviors of commonly used emerging memory devices as well as their application characteristics in various neuromorphic computing paradigms, including computing in memory (CIM) and other biologically plausible brain-like computing. Next, we introduce the principles of CIM and analyze its characteristics from the perspective of parallelism, precision, and signal domains. We also summarize some representative works on chip and system implementations. Additionally, we review the state of the art of other brain-like computing approaches, such as spiking neural networks (SNNs) and reservoir computing (RC). Finally, we provide insights and conclusions on the potential and obstacles of developing neuromorphic computing based on emerging memory, from both the device and system levels.

The efficacy of stem cell therapy is substantially compromised due to low cell survival rate and poor local retention post-delivery. These issues drastically limit the application of stem cells for ischemic limb therapy, which requires effective blood perfusion and skeletal muscle regeneration. Herein, based on microfluidic technology, an integrated stem cell and cytokine co-delivery system designed for functional ischemic limb salvage was constructed by first incorporating the myogenic cytokine, fibroblast growth factor 19 (FGF19), into microspheres composed of methacrylate gelatin (GelMA). Then adipose-derived stem cells (ADSCs) were highly absorbed into the porous structure of the microspheres, overcoming the insufficient loading efficiency and activities by conventional encapsulation strategy. The fabricated ADSCs/FGF19@μsphere system demonstrated a uniform size of about 180 μm and a highly porous structure with pore sizes between 20 and 40 μm. The resultant system allowed high doses of ADSCs to be precisely engrafted in the lesion and to survive, and achieved sustained FGF19 release in the ischemic region to facilitate myoblast recruitment and differentiation and myofibrils growth. Furthermore, the combination of ADSCs and FGF19 exhibited a positive synergistic effect which substantially improved the therapeutic benefit of angiogenesis and myogenesis, both in vitroand in vivo. In summary, a stem cell and cytokine co-delivery system with the properties of easy preparation and minimal invasiveness was designed to ensure highly efficient cell delivery, sustained cytokine release, and ultimately realizes effective treatment of ischemic limb regeneration.

Surface uplift of the Tibetan Plateau in the Cenozoic has dramatically affected evolutions of climate, environment, and biota in East Asia. The onset time of plateau uplift can be roughly constrained by the termination of the latest marine sedimentation, which shows large spatial differences in the Tibetan Plateau. In the northwest Tibetan Plateau of the western Kunlun area, the Tielongtan Group represents the latest marine sedimentation. However, the age of its depositional cessation is still unknown. For the first time, we conduct taxonomic, paleoecologic and paleogeographic studies of planktic foraminifera in the limestones of the Tielongtan Group. Despite poor preservation and low abundance, ten planktic foraminiferal species are identified, including Globotruncana falsostuarti, G. cf. linneiana, G. cf. ventricosa, Planohedbergella prairiehillensis, P. cf. yaucoensis, Pseudotextularia nuttalli, Planoheterohelix globulosa, P. cf. praenuttalli, Muricohedbergella holmdelensisand Whiteinella brittnensis. The foraminiferal assemblages suggest an age of late Campanian to Maastrichtian, indicating a shallow, neritic marine environment. Combining available research data from other regions, we show that these planktic foraminifera were widely distributed in the Tarim basin, southern Tibet, and the Mediterranean area during the latest Cretaceous. Our result of planktic foraminifera in the Tielongtan Group implies that the surface uplift of the western Kunlun area did not begin until after ∼76–66 Ma.

Boron nitride (BN) is an exceptional material, and among its polymorphs, two-dimensional (2D) hexagonal and three-dimensional (3D) cubic BN (h-BN and c-BN) phases are most common. The phase stability regimes of these BN phases are still under debate, and phase transformations of h-BN/c-BN remain a topic of interest. Here, we investigate the phase stability of 2D/3D h-BN/c-BN nanocomposites and show that the coexistence of two phases can lead to strong nonlinear optical properties and low thermal conductivity at room temperature. Furthermore, spark-plasma sintering of the nanocomposite shows complete phase transformation to 2D h-BN with improved crystalline quality, where 3D c-BN possibly governs the nucleation and growth kinetics. Our demonstration might be insightful in phase engineering of BN polymorph-based nanocomposites with desirable properties for optoelectronics and thermal energy management applications.

Embedded non-volatile memory (NVM) plays a significant role in modern information systems. Larger embedded NVM could empower a microcontroller unit (MCU) to run more advanced applications. Compared to conventional embedded NVM, e.g., Flash, Resistive random-access memory (ReRAM) has shown many advantages in speed, power, and cell size. However, heterogeneous integration of ReRAM with logic circuits still faces two key challenges: 1) integrating high-accuracy logic circuits and large-scale ReRAM with reasonable reliability and access speed; 2) achieving multiple bits per cell with acceptable circuit overhead. In this brief, we present a fully integrated 32-bit MCU monolithically integrated with 1Mb programming configurable ReRAM subsystem. Measured results show that the chip achieves >95% read accuracy and a 1.3% mean error for all targets in 3-bit mode. The CMOS circuits are fabricated on a 110nm CMOS process.

The line resistance (LR) in a large-scale memristor crossbar array can cause serious IR-drop problem, degrading the hardware deployment capability of neural networks (NNs). In this work, two innovation schemes from the level of software are proposed to mitigate the hardware IR-drop problem by intentionally modulating the NN activation function before deploying. The methods are evaluated over typical activation functions and various line resistances on MLP and LeNet-5 for MNIST recognition. Results show the methods can significantly improve the tolerance of NNs to IR-drop and recover the accuracy in some extent. The methods require no extra hardware overhead and reduce the complexity of peripheral circuits, which make them more achievable and attractive.

Memristive technology has been rapidly emerging as a potential alternative to traditional CMOS technology, which is facing fundamental limitations in its development. Since oxide-based resistive switches were demonstrated as memristors in 2008, memristive devices have garnered significant attention due to their biomimetic memory properties, which promise to significantly improve power consumption in computing applications. Here, we provide a comprehensive overview of recent advances in memristive technology, including memristive devices, theory, algorithms, architectures, and systems. In addition, we discuss research directions for various applications of memristive technology including hardware accelerators for artificial intelligence, in-sensor computing, and probabilistic computing. Finally, we provide a forward-looking perspective on the future of memristive technology, outlining the challenges and opportunities for further research and innovation in this field. By providing an up-to-date overview of the state-of-the-art in memristive technology, this review aims to inform and inspire further research in this field.

Pore structure plays an important role in the preservation and migration of shale gas. The pore structure of shale samples from the lower Palaeozoic Lujiaping and Longmaxi Formations in northeastern Chongqing was characterized by image analysis and fluid injection, and the porosity, homogeneity, and roughness were studied by using various fractal methods. The results indicate that compared with the shale facies of the Longmaxi Formation, the Lujiaping Formation has more developed organic matter pores, smaller macropore (>50 nm) volumes, smaller macropore specific surface areas, smaller mesopore (2–50 nm) volumes, larger mesopore specific surface areas, larger micropore (<2 nm) volumes, and micropore specific surface areas. The shale samples of the Lujiaping Formation are in the stage of micrographitization, lack a rigid skeleton, and are obviously influenced by other external factors such as compaction; there is no obvious correlation between pore structure and RO, TOC (total organic carbon), and clay and quartz of the Lujiaping Formation. However, the maturity of the shale samples of the Longmaxi Formation is in the high-maturity stage, which has not yet reached the organic carbonization stage or has a low carbonization degree and is less affected by compaction. The mesopore and micropore volumes and specific surface area have obvious negative correlations with RO, TOC, and quartz minerals and positive correlations with clay minerals. In addition, compared with the Longmaxi Formation shale, the Lujiaping Formation shale has larger fractal dimensions of macropores, mesopores, and micropores, which indicates that it has stronger heterogeneity, stronger pore roughness, and more complex morphology. At the same time, the fractal dimension of mesopores and micropores has a negative correlation with RO, TOC, and quartz. The main reason for the poor prospects of shale exploitation in the Lujiaping Formation may be pore shrinkage in the micrographitization stage.

In this paper, the deformation behavior and microstructure evolution of 300M steel are comprehensively studied by using the split-Hopkinson pressure bar technology, with an emphasis on the analytical estimate of material flow and microstructure distribution subjected to high strain rate. It is found that the grains have been significantly refined due to dynamic recrystallization (DRX) and major continuous DRX supplemented by few discontinuous ones governs the microstructure evolution. The dislocation activities associated with the angle change of grain boundaries and mechanical twinning dominate the deformation mechanism of 300M steel under high strain rate. Besides, nanoparticles in the form of carbide precipitation are uniformly distributed in the deformed zone, which strengthens the material by pinning the dislocations. A thermo-mechanical coupled analytical model considering dynamic recovery and dynamic recrystallization was innovatively developed, based on which the distribution of grain size and microhardness was predicted. The results indicated that the flow behavior described by the models and the calculated grain size as well as microhardness are proven to agree well with the results of experimental analysis.

Alpine revegetated dunes have been barely researched in terms of morphological change and migration within its regional aeolian environments. To reveal the sand-fixing and land-reforming mechanisms of artificial vegetation, we observed the morphology and migration of four dunes with four revegetated types (Hippophae rhamnoidesLinn., Salix cheilophilaSchneid., Populus simoniiCarr., and Artemisia desertorumSpreng.) using unpiloted aerial vehicle images and GPS (global positioning system) mapping in 2009 and 2018. Spatial analysis of GIS (geographic information system) revealed that the revegetated dunes exhibited a steady progression from barchan dune shapes to dome or ribbons shapes mainly through knap planation, wing amplification, and slope symmetrization. Generally, conditions of northern aspects, smaller slope degree, and larger altitude of unvegetated dunes would suffer more serious wind erosion. The southward movement of dune wings with a migration speed of 2.0–5.0 m/a and the alternating motion of sand ridges in eastwestern directions led greater stability in revegetated dunes. The moving distances of revegetated dunes remarkably changed in patterns of quadratic or linear function with depositional depth. Compared with unvegetated dunes, the near-surface wind velocity of revegetated dunes decreased by 20%–30%, which led to heavy accumulation in low-flat dunes and erosion in high-steep dunes, but all vegetation species produced obvious sand-fixing benefits (100%–450% and 3%–140% in the lower and higher dune scales of revegetated dunes, respectively) with decreasing sand transport rates and increasing coverages. In practice, the four vegetation species effectively anchored mobile dunes by adapting to regional aeolian environment. However, future revegetation efforts should consider optimizing dune morphology by utilizing H. rhamnoidesas a pioneer plant, S. cheilophilaand P. microphyllain windward and northward dune positions, and A. desertorumin a sand accumulative southward position. Also, we should adjust afforestation structure and replant some shrub or herbs in the higher revegetated dunes to prevent fixed dune activation and southward expansion.

Electron correlations often lead to emergent orders in quantum materials, and one example is the kagome lattice materials where topological states exist in the presence of strong correlations between electrons. This arises from the features of the electronic band structure that are associated with the kagome lattice geometry: flat bands induced by destructive interference of the electronic wavefunctions, topological Dirac crossings and a pair of van Hove singularities. Various correlated electronic phases have been discovered in kagome lattice materials, including magnetism, charge density waves, nematicity and superconductivity. Recently, a charge density wave was discovered in the magnetic kagome FeGe, providing a platform for understanding the interplay between charge order and magnetism in kagome materials. Here we observe all three electronic signatures of the kagome lattice in FeGe using angle-resolved photoemission spectroscopy. The presence of van Hove singularities near the Fermi level is driven by the underlying magnetic exchange splitting. Furthermore, we show spectral evidence for the charge density wave as gaps near the Fermi level. Our observations point to the magnetic interaction-driven band modification resulting in the formation of the charge density wave and indicate an intertwined connection between the emergent magnetism and charge order in this moderately correlated kagome metal.