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Motor imagery, one of the main brain-computer interface (BCI) paradigms, has been extensively utilized in numerous BCI applications, such as the interaction between disabled people and external devices. Precise decoding, one of the most significant aspects of realizing efficient and stable interaction, has received a great deal of intensive research. However, the current decoding methods based on deep learning are still dominated by single-scale serial convolution, which leads to insufficient extraction of abundant information from motor imagery signals. To overcome such challenges, we propose a new end-to-end convolutional neural network based on multiscale spatial-temporal feature fusion (MSTFNet) for EEG classification of motor imagery. The architecture of MSTFNet consists of four distinct modules: feature enhancement module, multiscale temporal feature extraction module, spatial feature extraction module and feature fusion module, with the latter being further divided into the depthwise separable convolution block and efficient channel attention block. Moreover, we implement a straightforward yet potent data augmentation strategy to bolster the performance of MSTFNet significantly. To validate the performance of MSTFNet, we conduct cross-session experiments and leave-one-subject-out experiments. The cross-session experiment is conducted across two public datasets and one laboratory dataset. On the public datasets of BCI Competition IV 2a and BCI Competition IV 2b, MSTFNet achieves classification accuracies of 83.62% and 89.26%, respectively. On the laboratory dataset, MSTFNet achieves 86.68% classification accuracy. Besides, the leave-one-subject-out experiment is performed on the BCI Competition IV 2a dataset, and MSTFNet achieves 66.31% classification accuracy. These experimental results outperform several state-of-the-art methodologies, indicate the proposed MSTFNet's robust capability in decoding EEG signals associated with motor imagery.

Macrocyclization is a critical strategy in rational drug design that can offer several advantages, such as enhancing binding affinity, increasing selectivity, and improving cellular permeability. Herein, we introduce MacGen, a web tool devised for structure-based macrocycle design. MacGen identifies exit vector pairs within a ligand that are suitable for cyclization and finds 3D linkers that can align with the geometric arrangement of these pairs to form macrocycles. To aid in the fast acquisition of appropriate linkers, we have built an indexed 3D linker database that includes linkers of various lengths and categories. MacGen provides comprehensive configurable parameters that enable users to obtain preferred linkers, meeting unique requirements in practical ligand design scenarios. We hope MacGen will serve as a handy tool that can rapidly explore potential macrocycle space. The MacGen server is freely accessible at https://macgen.xundrug.cn.

Monitoring lithium ions (Li+) in lithium-rich brine (LrB) is critical for metal recovery, yet challenges such as high ionic strength and gypsum-induced surface deterioration hinder the performance of potentiometric ion-selective electrode (ISE) sensors. This study advances the functionality of Li+ISE sensors and enables continuous monitoring of Li+concentration in LrB by introducing apolyelectrolyte multilayer (PEM) of poly(allylamine hydrochloride)/poly(sodium 4-styrenesulfonate) (PAH/PSS) that serves as an antigypsum scaling material to minimize nucleation on the sensor surface. With 5.5 bilayers of PAH/PSS coating, the Li+ISE sensors possess a high Nernst slope (59.14 mV/dec), rapid response (<10 s), and superior selectivity against competitive ions (Na+, log Ks= −2.35; K+, log Ks= −2.47; Ca2+, log Ks= −4.05; Mg2+, log Ks= −4.18). The impedance (85.1 kΩ) of (PAH/PSS)5.5-coated sensors is 1 order of magnitude lower than that of electrospray ion-selective membrane (E-ISM) Li+sensors (830 kΩ), attributed to the ultrathin (45.3 nm) and highly dielectric PAH/PSS bilayers. During a 15-day continuous monitoring test in LrB, the (PAH/PSS)5.5-coated Li+ISE sensors with their superhydrophilic and smooth surface diminish nucleation sites for scaling agents (e.g., Ca2+and SO42–) and consequently mitigate gypsum scaling. Moreover, a brine-tailored denoising data processing algorithm (bt-DDPA), coupled with the salinity-adjusted mathematical model with Lagrange interpolation, effectively captures Li+fluctuation by filtering out anomalies and reducing sensor drift in brine. Bt-DDPA alleviates the discrepancy between the sensor readings and the lab-based validation results by 46.06%. This study demonstrates that the integration of material advancement (PAH/PSS coating) with sensor data processing (bt-DDPA) bolsters continuous and accurate Li+monitoring in LrB, crucial for brine water treatment and resource recovery.

In recent years, motor imagery (MI) based on brain-computer interface (BCI) has gained the attention of researchers and been widely used in many fields, such as medical rehabilitation and entertainment. The temporal and spatial features of electroencephalography (EEG) are very important for MI-BCI classification. This study proposed a better spatiotemporal feature extraction method by combining the common spatial pattern (CSP) with a hybrid spatiotemporal attention convolutional neural network (HSTA-Net) model. The method first intercepts the MI-EEG into multiple time windows and projects them into the feature space and feeds into the HSTA-Net in parallel. Subsequently, the scaled dot product attention module and multi-head attention module are used to extract spatial feature attention. Finally, the features in multiple time windows obtained through HSTA-Net are integrated and used for classification. The proposed method achieves a classification accuracy of 77.3% on the BCI Competition IV2a dataset. The experimental results show that the proposed model has better classification performance. The CSP-HSTA-Net model can quickly adapt to the distribution of MI-EEG data during the training process, which increases the interclass distance of the extracted features and decreases its intraclass distance; it can better accomplish the MI-BCI classification task.

Corporate ESG performance is an important way for stakeholders to understand the corporate environment, social responsibility, and governance behavior. Under China’s implementation of the “carbon peak and carbon neutrality” dual carbon strategic background, ESG has become an important tool to promote the achievement of the “dual carbon” goals and high-quality development. How enterprises can reasonably and effectively improve their ESG performance and promote their green and sustainable development has become a major practical problem that urgently needs to be solved. Based on social learning and dynamic competition theories, this study takes Shanghai-Shenzhen A-share listed companies from 2011 to 2021 as research samples, empirically tests whether there is a peer effect in the ESG performance of listed companies in China, and examines the generation mechanism and influencing factors of the peer effect in the ESG performance of enterprises. The results show that: (i) There is an industry and regional peer effect on the ESG performance of enterprises, where the average ESG performance of other enterprises in the same industry and region, except for the focus enterprise, significantly affects the ESG performance of the focus enterprise.This core conclusion still holds true after robustness tests such as instrumental variable method, propensity score matching method, and first-order difference method to eliminate endogeneity issues, replace key measurement indicators, and control for macro factors. (ii) The mechanism analysis results show that the “information learning” and “competitive pressure” mechanisms promote the peer effect of ESG performance. (iii) Further research has found that institutional investors’ attention and government environmental regulations positively and significantly impact the peer effect of corporate ESG performance. (iv) Heterogeneity analysis shows that the ESG performance peer effect is more significant for large-scale, eastern, and state-owned enterprises than for small-scale, central, western, and non-state-owned enterprises. This study expands the boundaries of current ESG theory and empirical research, and the conclusions provide important policy implications for governments and enterprises.

The dynamic evolution in friction and wear can lead to interface failure and induce vibrations in pipeline inspection gauge. The friction behavior between polyurethane and X70 steel was evaluated with annular face contact under 0.042 to 0.07 MPa. The temperature increase was recorded using infrared cameras. Dynamic simulations of frictional heat and stress-strain were conducted using Abaqus software. The results indicate that in the initial stages, abrasive wear, stress (1.5 MPa), and frictional temperature rise (65 °C) of PU were observed in the inner-ring region at 0.056 MPa. The surface roughness (3.394 μm) of the PU became comparable to that of the steel ring (3.768 μm) due to abrasive wear. In the later stages, a higher frictional temperature (80 °C) and increased stress (3.8 MPa) were observed in the outer ring. Concurrently, Schallamach waves and ridge-like stripes indicative of stick-slip behavior emerged in the outer ring. The viscoelastic hysteresis effect is the fundamental cause of the dynamic evolution of the friction coefficient and wear. Frictional heat exacerbates the evolution of stick-slip behavior and increases fluctuations in the friction coefficient. The findings of this research can be utilized to analyze the causes of interface failure and vibration in pipeline inspection gauge.

The advancement of antimicrobial peptides (AMPs) as therapeutic agents is hindered by their poor selectivity. Recent evidence indicates that controlled disruption of the amphipathicity of α-helical AMPs may increase the selectivity. This study investigated the role of imperfect amphipathicity in optimizing AMPs with varied sequences to enhance their activity and selectivity. Among these, the lead peptide RI-18, characterized by an imperfectly amphipathic α-helical structure, demonstrated potent and broad-spectrum antibacterial activity without inducing hemolytic or cytotoxic effects. RI-18 effectively eliminated planktonic and biofilm-associated bacteria as well as persister cells and exhibited high bacterial plasma membrane affinity, inducing rapid membrane permeabilization and rupture. Notably, RI-18 significantly reduced bacterial loads without promoting bacterial resistance, highlighting its therapeutic potential. Overall, this study identified RI-18 as a promising antimicrobial candidate. The rational strategy of tuning imperfect amphipathicity to enhance the AMP activity and selectivity may facilitate the design and development of AMPs.

Previous studies showed that osa-miR168a, a plant miRNA rich in fruits and vegetables, had cross-kingdom biological effects on immunocytes, silkworms, and rodents. In this study, the effects of miR168a on mouse colitis induced by dextran sulfate sodium (DSS) were investigated. The results showed that miR168a oligomers were resistant during the process of food digestion, ending up with a residual concentration of 67.8 ± 11.2 fM in mouse intestines 4 h after oral gavage. More importantly, direct oral administration of the miRNA to the colitis mice significantly ameliorated the progression of the disease, as evidenced by the reduction in DAI score, histopathological lesions, and proinflammatory cytokines. Repairing intestinal barrier function by promoting the regeneration of TJ proteins and the mucus layer, suppressing oxidative stress and colonic inflammation via modulating Nrf2 and NF-κB signaling pathways, and restoring the imbalanced gut microbiota caused by DSS are proposed mechanisms behind the anticolitis activity of miR168a. This study provided new evidence of the cross-kingdom regulatory effects of dietary miRNAs, suggesting the potential of the plant miRNA for the prevention and treatment of inflammatory bowel diseases.

Polyimide with surface microstructures have a significant applications in fields such as electronics and aerospace. Appropriate processing methods playing a crucial role in achieving high-quality microstructures to meet the above application requirements. In this study, rectangular microstructures were fabricated on polyimide surfaces using ultra-precision ordinary planing (UPOP), ultrasonic elliptical vibration cutting (UEVC), and utra-precision fly-cutting (UPFC) methods, and both theoretical and experimental investigations were conducted. The characteristics of the three processing methods were analyzed, establishing the relationship between the chips, and evaluating the surface quality of the microstructures, cutting forces, and chip morphology. The results indicate that, compared to UPOP, UEVC can improve the processing quality of microstructures. At a cutting depth of 35 μm, the edge defects of microstructure grooves processed by UEVC are 39 μm smaller, and the burr height is approximately 5 μm lower than that of UPOP. However, when the cutting depth is greater than or equal to 70 μm, the burr height for UEVC is higher than that for UPOP, with the maximum difference being around 15 μm. The UPFC method achieves the best surface quality for microstructures and demonstrates significant effectiveness in suppressing burr formation. Burr height is only measured to be approximately 4 μm at a cutting depth of 35 μm, and burrs are virtually undetectable at larger cutting depths. The maximum cutting force of UPOP is 4.81 N, while the maximum cutting force of UPFC is only 0.021 N, making the cutting force of UPOP 229 times that of UPFC. The chips produced by UPOP are primarily serrated, whereas the chips produced by UPFC consistently exhibit a slender, elongated shape. A small material removal rate and high slope can prevent chip interactions, thereby enabling the use of UPFC to manufacture high-quality PI microstructures.

In steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs), various spatial filtering methods based on individual calibration data have been proposed to alleviate the interference of spontaneous activities in SSVEP signals for enhancing the SSVEP detection performance. However, the necessary calibration procedures take time, cause visual fatigue and reduce usability. For the calibration-free scenario, we propose a cross-subject frequency identification method based on transfer superimposed theory for SSVEP frequency decoding. First, a multi-channel signal decomposition model was constructed. Next, we used the cross least squares iterative method to create individual specific transfer spatial filters as well as source subject transfer superposition templates in the source subject. Then, we identified common knowledge among source subjects using a prototype spatial filter to make common transfer spatial filters and common impulse responses. Following, we reconstructed a global transfer superimposition template with SSVEP frequency characteristics. Finally, an ensemble cross-subject transfer learning method was proposed for SSVEP frequency recognition by combining the source-subject transfer mode, the global transfer mode, and the sine-cosine reference template. Offline tests on two public datasets show that the proposed method significantly outperforms the FBCCA, TTCCA, and CSSFT methods. More importantly, the proposed method can be directly used in online SSVEP recognition without calibration. The proposed algorithm was robust, which is important for a practical BCI.

True random number generators (TRNGs) are commonly used in hardware security for secure authentication, data encryption, etc. The raw random numbers often exhibit defects. The most commonly observed defects are bias and correlations. Post processing techniques have been developed to address them. The von Neumann method addresses bias, but it requires input that is uncorrelated and has an identical distribution. On the other hand, the Markov chain can address correlation but introduce bias. In this work, we research the lightweight combination of two techniques. We verified that MKV2(QL4)/VN2 performs well for both Markov and non-Markov model bitstreams. MKV1(QL8)/VN8W is effective for the Markov model. The randomness is verified by NIST SP 800–22 and 800-90B, and ENT, respectively. Both of these circuits require only 16 bits of memory, which is 12 times smaller than in previous work. MKV1(QL8)/VN8W is implemented using 65-nm CMOS. A prototype chip demonstrates a minimum energy consumption of 0.149 pJ/bit at 0.45V. When applied to a latch-based TRNG, it can double the operation frequency thanks to the enhanced decorrelation. The total energy consumption is reduced by 21%.

Integrating the dual functionalities of a photodetector and photonic synapse into a single device is challenging due to their conflicting requirements for photocurrent decay rates. This study addresses this issue by seamlessly depositing transparent indium tin oxide (ITO) electrodes onto self-oriented copper hexadecafluoro-phthalocyanine (F16CuPc) nanowires growing horizontally along hot-stamped periodic nanogrooves on a transparent flexible polyimide plastic film. This in-situ-fabricated device achieves bending-stable dual functionalities through wavelength regulation while maintaining high transparency and flexibility. Upon exposure to 450–850 nm light, the device exhibits a rapid and sensitive photoresponse with excellent bending stability, making it ideal for optical sensing in both visible and near-infrared spectra. More importantly, the device exhibits a bending-stable excitation postsynaptic current when exposed to light spikes below 405 nm. This enables the successful emulation of various biological synaptic functionalities, including paired-pulse facilitation, spike-number-dependent plasticity, spike-duration-dependent plasticity, spike-rating-dependent plasticity, configurable plasticity between short-term plasticity and long-term plasticity, and memory learning capabilities. Utilizing this device in an artificial neural network achieves a recognition rate of 95% after 57 training epochs. Its ability to switch between photodetection and synaptic modes by adjusting the light wavelength marks a significant advancement in the field of multifunctional flexible electronics based on nanowire arrays.

The exploitation of small molecules as fluorescence sensors represents a minimalistic solution toward the sensing of hazardous volatile organic compounds (VOCs). Compared with the conventional aggregation-induced emitting sensors, the carborane (Cb)-based sensors have exhibited multiple advantages and improved quantitative fluorescence (QF) sensing abilities toward the gaseous VOCs. However, in the current Cb-based sensors, the localization of a single responsive site toward VOCs remains less focused, which results in a bias in the trace detection and short-range testing windows. In this work, we synthesized two pyrene-alkynylated carboranes (Py-1and Py-2) and investigated their photophysical properties in different cases. We found that Py-1and Py-2in the films were consistently self-assembled through π···π aggregation of pyrenylethynyl moieties. Theoretical modeling showed that the highly emissive π···π aggregates were thermodynamically stable and their responsive sites toward VOCs were localized on the electron-poor phenyl or fluorenyl groups. As a result, the Py-1and Py-2films showed remarkable emission-off sensibilities toward NEt3vapors via a major route of photoinduced electron transfer. The optimized QF sensor Py-2showed linear emission-off response toward three types of static amine vapors in long concentration ranges (1.78–90 g/m3at most), and the limit of detection could be lowered to 99 mg/m3in the in situ sensing.

Phototherapy has demonstrated substantial development because in the second near-infrared (NIR-II) window it has a larger tissue penetration and fewer adverse consequences. In this work, a particular kind of NIR-II responsive Fe-doped carbon nanoparticles (FDCNs) is synthesized using a one-pot hydrothermal method for combined photothermal and chemodynamic therapy. The mesoporous nanostructure of FDCN, which has a size distribution that exceeds 225 nm, allows for effective acidification. The iron ions released from these nanoparticles can catalyze the decomposition of hydrogen peroxide (H2O2) into hydroxyl radical (•OH) for chemodynamic therapy (CDT). In addition to their CDT utility, FDCN can effectively adsorb and transform 1064 nm light into local heat, achieving a photothermal conversion efficacy (PCE) of 36.3%. This dual functionality not only allows for the direct eradication of cancer cells through photothermal therapy (PTT) but also enhances the chemodynamic reaction, creating a synergistic effect that amplifies the therapeutic outcome. The FDCN has demonstrated remarkable anticancer activity in both cellular and animal tests without incurring major systemic toxicity. This suggests that the compound has great promise for use in clinical cancer therapy.

Given the pressing depletion of lithium resources, sodium-ion batteries (SIBs) stand out as a cost-effective alternative for energy storage solutions in the near future. Layered transition metal oxides (LTMOs) emerge as the leading cathode materials for SIBs due to their superior specific capacities and abundant raw materials. Nonetheless, achieving long-term stability in LTMOs for SIBs remains a challenge due to the inevitable structural degradation during charge–discharge cycles. The complexity and diversity of cation configurations/superstructures within the transition metal layers (TMO2) further complicate the understanding for newcomers. Therefore, it is critical to summarize and discuss the factors leading to structural degradation and the available strategies for enhancing LTMOs’ stability. In this review, the cationic configurations of TMO2layers are introduced from a crystallographic perspective. It then identifies and examines four key factors responsible for structural decay, alongside the impacts of various modification strategies. Finally, more effective and practical research approaches for investigating LTMOs have been proposed. The work aims to enhance the comprehension of the structural deterioration of LTMOs and facilitate a substantial improvement in their cycle life and energy density.

Sapphire exhibits typical anisotropy due to its unique crystal structure, and there are significant property differences in different processing areas during the machining of curved sapphire component, making it essential to investigate the anisotropy in the removal process of sapphire materials for efficient curved surface machining. In this study, the force and acoustic emission (AE) signals were monitored at different scratch stages and orientations, and the anisotropic characteristics of ductile-brittle transition (DBT) depths, material removal morphology, and subsurface crack propagation direction were analyzed. The results indicate that the anisotropy of force signals and DBT depths is mainly reflected in the differences between crystal planes. In addition, the removal morphology of materials with different crystal orientations showed smooth scratch grooves with unclear anisotropic characteristics during the ductile removal (DR) stage, while the crack propagation direction with different scratch orientations during the DBT stage showed significant anisotropic characteristics. The brittle removal (BR) stage is characterized by brittle spalling in the form of shell-like, blocky, and fishbone spalling, and there is a significant anisotropic difference in the spalling shapes on both sides of the scratch. The original AE signals, Fast Fourier Transform (FFT), and Short Time Fourier Transform (STFT) results show that the AE frequencies of different crystal planes are different, and the differences in different scratch orientations on the same crystal plane are reflected in the peak amplitude. Finally, the propagation directions of subsurface cracks with different directions are anisotropic due to different scratching crystal planes, and propagate mainly along directions of 15°, 29°, 32°, 58°, and 105°.

Fragment growing is an important ligand design strategy in drug discovery. In this study, we present FragGrow, a web server that facilitates structure-based drug design by fragment growing. FragGrow offers two working modes: one for growing molecules through the direct replacement of hydrogen atoms or substructures and the other for growing via virtual synthesis. FragGrow works by searching for suitable fragments that meet a set of constraints from an indexed 3D fragment database and using them to create new compounds in 3D space. The users can set a range of constraints when searching for their desired fragment, including the fragment’s ability to interact with specific protein sites; its size, topology, and physicochemical properties; and the presence of particular heteroatoms and functional groups within the fragment. We hope that FragGrow will serve as a useful tool for medicinal chemists in ligand design. The FragGrow server is freely available to researchers and can be accessed at https://fraggrow.xundrug.cn.

Large language models have recently brought a massive storm on modern society in all fields. While many view them as mere search engines for specific answers or text refinement tools like a chatbot, their broader applications remain largely unexplored. These large language models, consisting of billions of interconnected neurons, derived from all knowledge of the human, possess the remarkable ability to engage in smooth and precise conversations with individuals across the globe. Human-like intelligence enables them to address modern challenges and display immense potential in various scientific domains. In this perspective, we delve into the potential applications of modern large language model and its future iterations within the field of catalysis, aiming to shed light on how these AI-driven models can contribute to a deeper understanding of catalysis science and the intelligent design of catalysts.

Previous studies showed that cal-miR2911, featuring an atypical biogenesis, could target genes of virus and in turn inhibit virus replication. Given its especial sequence motif and cross-kingdom potential, the stability of miR2911 under digestive environment and its impact on intestinal microbes in mice were examined. The results showed that miR2911 was of considerable stability during oral, gastric, and intestinal digestion. The coingested food matrix enhanced its stability in the gastric phase, contributing to the existence of miR2911 in mouse intestines. The survival miR2911 promoted the growth of Bifidobacteriumin mice and maintained the overall composition and diversity of the gut microbiota. miR2911 specifically entered the cells of Bifidobacterium adolescentisand potentially modulated the gene expression as evidenced by the dual-luciferase assay. The current study provided evidence on the cross-kingdom communication between dietary miRNAs and gut microbes, suggesting that modulating target bacteria using miRNAs for nutritional and therapeutic ends is promising.

Commercial fiber Bragg grating (FBG) sensors typically rely on require plastics, acrylates, or glues for encapsulation and adhesion, making them susceptible to aging, creep, and high-temperature intolerance. To overcome these issues, this study introduces an electrochemical deposition process aimed at creating adhesive-free sensors by embedding FBGs into metal structures. This approach enhances the operational range and lifespan of the sensor. The embedding method facilitates the detection of bending strains by the FBG. Consequently, a temperature-insensitive inclination sensor is designed and fabricated. Two FBGs are embedded on either side of a metal strip, precisely aligned with its central axis, to measure bending strain. When bending stress is applied to the metal steel, the two FBGs experience opposite strains, causing their central wavelengths to drift in opposite directions. Analyzing the wavelength shift difference between the two FBGs yields a sensor with higher inclination sensitivity and temperature insensitivity. The experimental results indicate that the designed sensor has a high sensitivity of 42.76 pm/°, resolution of 0.0023°, and linearity of 0.9994. Temperature-response tests demonstrate the excellent temperature insensitivity of the fabricated sensor, allowing for reliable operation in high-temperature conditions. Furthermore, this metal encapsulation method exhibits significant potential for expanding the applications of FBGs.

In steady-state visual evoked potentials (SSVEPs) decoding algorithms, applying filter banks techniques and incorporating time-local information have been proven to significantly enhance SSVEP recognition performance. However, the challenge of setting time-local parameters for each subband within the filter banks has hindered the effective integration of these two techniques, thereby limiting improvements in SSVEP algorithm performance. To address this issue, we propose an SSVEP recognition framework that combines filter banks and time-local transformation (CFBTT). This framework is based on our hypothesis that increasing the energy distribution of SSVEP components within each subband can enhance algorithm performance. Therefore, we determine the time-local information parameters for each subband based on the differences in energy distribution before and after the time-local transformation and develop two parameter optimization methods designed for training-free and training algorithms, ${M}1$ and ${M}2$ , effectively integrating filter banks techniques with time-local transformation. Experimental results on two large-scale SSVEP datasets demonstrate that applying the CFBTT framework leads to significant improvements in recognition performance for both training-free and training algorithms. The enhanced performance of multiple state-of-the-art algorithms validates our hypothesis and demonstrates that our framework possesses strong generalization capabilities and broad applicability.

With the development of autonomous driving technology, some special vehicles should also be developed into autonomous driving vehicles. The robotic vehicles are widely used in engineering operations, and the robotic manipulator is a common tool mounted on robotic vehicles. The autonomous driving of robotic vehicles requires not only automatic movement, but also the automation of the robotic manipulator drive system. Therefore, this paper proposes an adaptive trajectory tracking control of manipulator based on sliding mode control (SMC), which can more accurately describe the joint space movement of the manipulator system under multi task coupling and external interference. Considering the multi-tasking situation, the influence of obstacle avoidance and base tilt on the trajectory of manipulator is investigated by using extreme learning machine (ELM), and the framework of machine learning is established. The output based on ELM-SMC is used as the bottom-layer control. Then an effective trajectory compensation method is proposed as the top-level control to avoid the error accumulation in the periodic repeated operation of the manipulator. Finally, the application effect of trajectory error tracking and compensation of manipulator in complex tasks of dynamic environment is verified by simulation and experiments, which lays a foundation for the stability control of manipulator in practical complex engineering applications. Note to Practitioners—The motivation of this paper is to solve the problem of poor trajectory tracking control accuracy of the manipulator caused by the change of the direction of the manipulator base. It is suitable for the high-precision position control of the manipulator assembled on the mobile robotic vehicle. Nowadays, the autonomous driving technology is developing rapidly, and the automation of traffic will be widely popularized in the future. Therefore, we should not only pay attention to the autonomous driving function of passenger vehicles, but also give consideration to the autonomous driving technology of robotic vehicles applied in engineering. It not only needs to complete the driving function, but also needs to ensure the reliable operation of the manipulator. In this paper, we mathematically deduce the control model of the manipulator. Then, we use SMC as the trajectory tracking control method of the manipulator, and use ELM to estimate the trajectory error caused by the end trajectory of the manipulator in the non-horizontal state of the base. Furthermore, we propose an optimization algorithm to optimize the compensation coefficient of cubic spline and give a reasonable compensation scheme. Through simulation and experiments, it is preliminarily verified that the schemes proposed in this paper are feasible, but they lack the application verification in the actual robotic vehicles, and the model fitting part of machine learning is completed with the help of the host computer, and the calculation ability of the controller of the actual manipulator may also be the limitation of completing this scheme.

The finite control set model predictive current control involves addressing challenges related to greater cost function values and inaccurate current prediction models, which can adversely impact the steady-state performance in permanent magnet synchronous motor (PMSM). A robust amplitude control set model predictive current control method (RACSMPCC) is proposed in this study, aiming to enhance prediction accuracy and control precision. This method introduces a rotation coordinate system amplitude control set to reduce the cost function value and improve current control precision. Then, a nonlinear extended state observer (NESO) is employed to observe and compensate for comprehensive disturbances in the current prediction equation, thereby improving the accuracy of the current prediction. Simultaneously, a parameter configuration method in the complex frequency domain is proposed for NESO, and its influence on the disturbance rejection performance of the system is analyzed. Hardware experiments on a PMSM are conducted to validate the effectiveness of RACSMPCC in reducing cost function and improving current control accuracy.

In urban pavement crack detection, the single detection platform with visible light sensors is mostly used for detection. Visible light sensors are highly susceptible to lighting conditions. In low-light environments or areas with shadows over the cracks, this may result in false positives or missed detections of pavement cracks. This article utilizes an image acquisition system that integrates multiple detection platforms, including visible light and infrared sensors, to enhance both the efficiency and accuracy of pavement crack detection. We propose an automatic pavement crack detection approach that uses the similarities and differences between visible light images and infrared image features [multisource image fusion based on similarity and difference (MSFSD)] to achieve all-weather, real-time detection of pavement cracks. MSFSD mainly includes multisource image fusion and pixel-level crack detection. In multisource image fusion, a generative adversarial network (C-GAN) with skip connections and dilated convolutional blocks (CRes2Net blocks) is constructed. The structural similarity index (SSIM) function and the sum of the correlations of differences (SCD) function are used as the loss function of the generator, allowing the fusion image to retain more details and edge information in the source image. In pixel-level crack detection, a crack detection model is constructed, and CRes2Net is used as the core network to obtain pixel-level labels and perform semantic segmentation. It is verified by experiments that MSFSD achieves 85.1% precision and 88.4% recall in different types of crack detection. The MIoU and detection efficiency of MSFSD are 9% and 48% higher than those of other methods, respectively.

This article presents a true random number generator (TRNG) that achieves high entropy generation across wide voltage and temperature (VT) range (0.3–1.0 V, −40 °C to 110 °C) in a single latch-based entropy source (ES). In the ES, static inverter selection technique to minimize the mismatch between the paired inverters, and noise enhancement methods to increase the root mean square (rms) of noise voltage ( $\sigma _{n}$ ) are implemented for good randomness and robustness. In a 130-nm CMOS technology, the TRNG occupies 5343 $\mu \text{m} ^{\mathrm{ 2}}$ and consumes 0.116 pJ/bit at 0.3 V including an on-chip von Neumann post-processing circuit. The cryptographic quality of TRNG’s output is verified by National Institute of Standards and Technology (NIST) SP800-22 tests. Up to 325 mV $V$ pp noise injection attack tolerance is confirmed by power supply frequency injection attack. And an equivalent 20-year life at 0.3 V, 25 °C is verified by accelerated NBTI aging test.

New evidence reveals that bol-miR159, an miRNA rich in fruits and vegetables, cross-kingdomly functions in mammalian bodies. However, whether the miRNA could regulate gut microbiota remains unclear. Here, the effect of miR159 on mouse intestinal microbes was comprehensively examined. The results showed that supplementation of miR159 to the chow diet significantly enhanced the diversity of mouse gut microbiota without causing pathological lesions or inflammatory responses on the intestines. At the phylum level, miR159 increased the abundance of Proteobacteria and decreased the Firmicute-to-Bacteroidetes (F/B) ratio. miR159 had prebiotic-like effects on mouse gut microbiota, as it promoted the growth of the bacteria that is beneficial for maintaining gut health. The miRNA can target bacteria genes and get into the bacteria cells. The data provide direct in vivoevidence on the crosstalk between plant miRNAs and intestinal microbes, highlighting the potential for miRNA-based strategies that modulate gut microbes to improve host health.

The DNA-encoded library (DEL) is a powerful hit-generation tool in drug discovery. This study describes a new DEL with a privileged scaffold quinazolin-4(3H)-one developed by a robust DNA-compatible multicomponent reaction and a series of novel glutathione S-transferase (GST) inhibitors that were identified through affinity-mediated DEL selection. A novel inhibitor 16was subsequently verified with an inhibitory potency value of 1.55 ± 0.02 μM against SjGST and 2.02 ± 0.20 μM against hGSTM2. Further optimization was carried out via various structure–activity relationship studies. And especially, the co-crystal structure of the compound 16with the SjGST was unveiled, which clearly demonstrated its binding mode was quite different from the known GSH-like compounds. This new type of probe is likely to play a different role compared with the GSH, which may provide new opportunities to discover more potent GST inhibitors.

The steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) has received extensive attention in research for the less training time, excellent recognition performance, and high information translate rate. At present, most of the powerful SSVEPs detection methods are similarity measurements based on spatial filters and Pearson’s correlation coefficient. Among them, the task-related component analysis (TRCA)-based method and its variant, the ensemble TRCA (eTRCA)-based method, are two methods with high performance and great potential. However, they have a defect, that is, they can only suppress certain kinds of noise, but not more general noises. To solve this problem, a novel time filter was designed by introducing the temporally local weighting into the objective function of the TRCA-based method and using the singular value decomposition. Based on this, the time filter and (e)TRCA-based similarity measurement methods were proposed, which can perform a robust similarity measure to enhance the detection ability of SSVEPs. A benchmark dataset recorded from 35 subjects was used to evaluate the proposed methods and compare them with the (e)TRCA-based methods. The results indicated that the proposed methods performed significantly better than the (e)TRCA-based methods. Therefore, it is believed that the proposed time filter and the similarity measurement methods have promising potential for SSVEPs detection.

Under disastrous earthquake conditions, the bolted spherical joints in spatial steel grid structures are prone to bending, breaking, and other failure modes, and their failure has obvious ultralow cycle fatigue failure characteristics. In this paper, quasistatic tests were carried out on three groups (nine specimens in total) of bolted spherical joints with joints at both ends under uniaxial cyclic loading. The failure characteristics, hysteretic behaviour, bearing capacity degradation, stiffness degradation, and energy compression capacity of bolted spherical joints under three loading schemes were studied. At the same time, the change rule of the rotation angle with the number of cycles was obtained. The experimental study shows that each member experiences characteristic moments of high-strength bolt bending, cracking, crack propagation, and a loud crack and that bolt failure occurs at the middle bolt ball connection. The ultimate ultralow cycle fatigue failure of the specimen occurs after more than 10 cycles, and the energy compression capacity is poor due to the small volume of high-strength bolts and sleeves. The "pinching phenomenon" of the hysteresis curve of the bolted spherical joint is serious. The tension area of the hysteresis curve increases, and the fullness of the compression area decreases, with increasing tensile amplitude. Based on the cumulative energy compression, a damage model suitable for bolted spherical joints under cyclic loading is proposed. From the experimental data, a correlation formula between the angle change rate and the damage model was established when the middle bolt ball of the connection specimen experienced a tension–compression cyclic load. This formula provides a reference for the evaluation of bolted spherical joints of bolt ball grid structures after an earthquake.

Fast and proper treatment of the tautomeric states for drug-like molecules is critical in computer-aided drug discovery since the major tautomer of a molecule determines its pharmacophore features and physical properties. We present MolTaut, a tool for the rapid generation of favorable states of drug-like molecules in water. MolTaut works by enumerating possible tautomeric states with tautomeric transformation rules, ranking tautomers with their relative internal energies and solvation energies calculated by AI-based models, and generating preferred ionization states according to predicted microscopic pKa. Our test shows that the ranking ability of the AI-based tautomer scoring approach is comparable to the DFT method (wB97X/6-31G*//M062X/6-31G*/SMD) from which the AI models try to learn. We find that the substitution effect on tautomeric equilibrium is well predicted by MolTaut, which is helpful in computer-aided ligand design. The source code of MolTaut is freely available to researchers and can be accessed at https://github.com/xundrug/moltaut. To facilitate the usage of MolTaut by medicinal chemists, we made a free web server, which is available at http://moltaut.xundrug.cn. MolTaut is a handy tool for investigating the tautomerization issue in drug discovery.

Designing functional binders is demonstrated an effective solution to address the serious issues of active iodine dissolution and polyiodide shuttle faced by aqueous zinc-iodine batteries (AZIBs). Herein, a series of linear-chain polysaccharides are systematacially investigated as the potential water-soluble binders for iodine-loading cathode of AZIBs. According to the results of spectral characterizations and theoretical calculations, SA binder, as the representative of polysaccharides, is verified with strong chemisorption capability and high binding energy for the iodine intermediates owing to the existence of active ether and carboxyl groups, which contributes to suppress the polyiodide shuttle and active iodine dissolution. Meanwhile, the lower Gibbs free energy values indicate the introduction of SA binder is conducive to boost the iodine redox reaction kinetics of iodine-loading cathode. Benefitting from these merits, the Zn//CAC@I2batteries with SA biner delivers superior self-discharge resistance capability, excellent rate performance, and ultra-durable cycling stability with high reversible capacity of 105.2 mAh g−1after 8000 cycles at 1 A g−1and 86.3 mAh g−1after 40,000 cycles at 5 A g−1, respectively. This work will enlighten the research and development of binder materials for advanced iodine-based energy storage devices, and facilitate the practical application of both polysaccharide binders and AZIBs.

The development of effective and cost-efficient catalysts for the hydrogen production through methanolysis of ammonia borane (AB) is a hot topic in the field of energy catalysis. In this study, a simple method has been developed to construct a low-cost Z-type heterojunction by depositing CuO nanoparticles on the surface of NiO nanosheets. The as-prepared CuO/NiO composites exhibit prominent activity towards AB methanolysis. The hydrogen generation rate (HGR) is 3.00 Lhydrogenmin−1gcat.−1when the optimal CuO/NiO sample functions as a catalyst, which is increased to 3.93 Lhydrogenmin−1gcat.−1with the assistance of visible light illumination. The enhancement of catalytic activity are attributed to the variation of the electronic structure of the catalysts. With the formation of Z-type heterojunction, the valence band electrons of CuO and NiO are excited and transfer to their respective conduction bands under light illumination. Subsequently, the conduction band electrons of CuO recombine with the valence band holes of NiO, leading to an accumulation of electrons at the conduction band of NiO. Consequently, the electron density of the Ni sites is increased, which enhances AB methanolysis. This study can provide new insights for the design and synthesis of cost-effective and highly efficient direct Z-type heterojunction catalysts for hydrogen production through AB methanolysis. Moreover, this study is also helpful for the understanding of how visible light enhances AB methanolysis.

Herein, we coalesced a poly(acrylamide-co-N-Acryloyl phenylalanine)/polyacrylamide double-network (P(AM-co-APA)/PAM DN) hydrogel with a photonic crystal array, fabricating a mechanochromic sensor for application in flexible medical instruments by naked eye monitoring. The intensified mechanical properties of the DN hydrogel were proved by the mechanical property tests, which are attributed to the interactions of chemical bonds and hydrogen bonds between the two polymer networks. In the range of stress from 0 to 328 kPa, the reflected light wavelength of this sensor changed from 659 to 480 nm and the color changed from red to blue in response; in the range of pressure from 0 to 85 kPa, the sensor exhibited a spectrum changing from 658 nm to 467 nm, covering almost the whole visible color range. The prepared sensor was incorporated into medical instruments including the femoral artery hemostat and bandage to indicate pressure and tensile stress in practical applications. Within the appropriate pressure for wound recovery, the sensitivity and correlation between the external stimulus of pressure and wavelength of this integrated sensor were 5.58 nm·kPa–1and over 0.99, respectively. Ultimately, the sensor proved to be tough, sensitive, and durable, showing a broad prospect of a series of future applications.

The power amplifier (PA) is an indispensable part of ultra-wideband (UWB) orthogonal frequency division multiplexing (OFDM) systems. Nevertheless, it will produce nonlinear distortions, degrading the accuracy of channel estimation and thus the performance of the system. To improve the system performance, it is essential to investigate channel estimation using distorted signals. Taking advantage of the sparsity of the UWB channel, a sparse channel estimation method using distorted signals is proposed in this letter, referred to as sparse channel estimation combined with power amplifier nonlinear cancellation (SCE-PANC). Within SCE-PANC, the measurement matrix is constructed only with pilot subcarriers and nonlinear distortions will be eliminated iteratively. The numerical results show that the proposed method has a better performance than the state-of-the-art methods.

Worn tools would wreak havoc on the surface integrity of workpieces in precision machining. This article develops a new system for ON-machine measurements of tool topography with micrometer accuracy based on the edge reversal principle. An array of experiments has been carried out to verify the system performance amenable to measuring various tools. An initial database of tools has been established as well for rapidly calculating tool wear and recording the topography characteristics of the different stages of tool wear. The ON-machine measurement system is capable of achieving an expanded measurement uncertainty of $0.6 \mu \text{m}$ for tools with corner radii from 200 to $800 \mu \text{m}$ . The work provides equipment and data basis for efficient ON-machine measurement of tool topography, which is beneficial to the prediction and management of tool life intelligently.

Chemodynamic therapy (CDT) is an emerging therapeutic modality to treat cancer based on magnetite nanozymes. However, the low catalytic capacity and single-mode treatment limit its therapeutic efficacy. Herein, a kind of carbon-coated magnetite nanoclusters (CCMNCs) with high absorbance in the second near-infrared (NIR-II) window has been developed for NIR-II photothermal-enhanced chemodynamic anti-cancer therapy. Such CCMNCs with a size of about 58 nm were synthesized by a one-step solvothermal method using ferrocene as a single precursor. The CCMNCs not only serve as a contrast agent for tumor-targeted magnetic resonance imaging, but also absorb and convert NIR-II irradiation into local heat for the hyperthermic killing of cancer cells. The ferrous and iron ions produced from the CCMNCs in acid solution catalyze the Fenton reaction to generate hydroxyl radicals (·OH) for CDT and reduce the intracellular glutathione that exerts as free radical scavengers. The reported CCMNCs demonstrate a superior therapeutic efficacy through the synergistic effect of a combined chemodynamic-photothermal treatment.

Panax notoginseng, a traditional medicinal and edible plant, is widely used in medicine, health care, cosmetics, and other industries. Affected by the discrepancy between market supply and demand and price, the adulteration of P. notoginsengproducts with other plant-derived ingredients occurs at times. With the continuous development of technologies such as spectroscopy, chromatography, and DNA barcoding, the detection techniques for rapid and sensitive determination of the authenticity identification and origin of P. notoginsenghave become more diversified to meet the needs of different regulatory goals and could effectively control practices that mislead consumers and promote false labeling. This review analyzes and summarizes the existing technologies for determining the authenticity and origin of P. notoginsengfrom these three aspects: morphological, chemical, and molecular biology methods from the literature since 2001; on this basis, the current problems and future research directions are discussed to provide a reference for the establishment of rapid and accurate methods to verify authenticity and origin to promote the further development and improvement of quality control technology systems for P. notoginseng.

OBJECTIVE--Childhood obesity strongly predisposes to some adult diseases. Recently, genome-wide association (GWA) studies in Caucasians identified multiple single nucleotide polymorphisms (SNPs) associated with BMI and obesity. The associations of those SNPs with BMI and obesity among other ethnicities are not fully described, especially in children. Among those previously identified SNPs, we selected six (rs7138803, rs1805081, rs6499640, rs17782313, rs6265, and rs10938397, in or near obesity-related genes FAIM2, NPC1, FTO, MC4R, BDNF, and GNPDA2, respectively) because of the relatively high minor allele frequencies in Chinese individuals and tested the associations of the SNPs with BMI and obesity in Chinese children. RESEARCH DESIGN AND METHODS--We investigated the associations of these SNPs with BMI and obesity in school-aged children. A total of 3,503 children participated in the study, including 1,229 obese, 655 overweight, and 1,619 normal-weight children (diagnosed by the Chinese age- and sex-specific BMI cutoffs). RESULTS--After age and sex adjustment and correction for multiple testing, the SNPs rs17782313, rs6265, and rs10938397 were associated with BMI (P = 1.0 x [10.sup.-5], 0.038, and 0.00093, respectively) and also obesity (P = 5.0 x [10.sup.-6], 0.043, and 0.00085, respectively) in the Chinese children. The SNPs rs17782313 and rs10938397 were also significantly associated with waist circumference, waist-to-height ratio, and fat mass percentage. CONCLUSIONS--Results of this study support obesity-related genes in adults as important genes for BMI variation in children and suggest that some SNPs identified by GWA studies in Caucasians also confer risk for obesity in Chinese children. Diabetes 59:3085-3089, 2010, In recent years, the prevalence of adult and childhood obesity has been increasing in most parts of the world, including China (1,2). Overweight children show a higher risk for severe [...]

Background: Angiotensin-receptor blockers (ARBs) blunt progression of advanced diabetic nephropathy, but their long-term renal effects in other patients are not clear. Objective: To examine the long-term renal effects of telmisartan versus placebo in adults at high vascular risk. Design: Randomized trial. Patients were randomly assigned by a central automated system between November 2001 and May 2004 and were followed until March 2008. Participants and investigators were blinded to intervention status. Setting: Multicenter, multinational study. Patients: 5927 adults with known cardiovascular disease or diabetes with end-organ damage but without macroalbuminuria or heart failure who cannot tolerate angiotensin-converting enzyme inhibitors. Intervention: Telmisartan, 80 mg/d (n = 2954), or matching placebo (n = 2972) plus standard treatment for a mean of 56 months. Measurements: Composite renal outcome of dialysis or doubling of serum creatinine, changes in estimated glomerular filtration rate (GFR), and changes in albuminuria. Results: No important difference was found in the composite renal outcome with telmisartan (58 patients [1.96%]) versus placebo (46 patients [1.55%]) (hazard ratio, 1.29 [95% CI, 0.87 to 1.89]; P = 0.20). Among the telmisartan and placebo groups, 7 and 10 patients had dialysis and 56 and 36 patients had doubling of serum creatinine, respectively (hazard ratio, 1.59 [CI, 1.04 to 2.41]; P = 0.031). Albuminuria increased less with telmisartan than with placebo (32% [CI, 23% to 41%] vs. 63% [CI, 52% to 76%]; P< 0.001). Decreases in estimated GFR were greater with telmisartan than with placebo (mean change in estimated GFR, -3.2 mL/min per 1.73 [m.sup.2] [SD, 18.3] vs. -0.26 mL/min per 1.73 [m.sup.2] [SD, 18.0]; P < 0.001). Limitation: Only 17 participants had dialysis. Conclusion: In adults with vascular disease but without macroalbuminuria, the effects of telmisartan on major renal outcomes were similar to those of placebo. Primary Funding Source: Boehringer Ingelheim.

Most soil quality measurements have been limited to laboratory-based methods that suffer from time delay, high cost, intensive labor requirement, discrete data collection, and tedious sample pretreatment. Real-time continuous soil monitoring (RTCSM) possesses a great potential to revolutionize field measurements by providing first-hand information for continuously tracking variations of heterogeneous soil parameters and diverse pollutants in a timely manner and thus enable constant updates essential for system control and decision-making. Through a systematic literature search and comprehensive analysis of state-of-the-art RTCSM technologies, extensive discussion of their vital hurdles, and sharing of our future perspectives, this critical review bridges the knowledge gap of spatiotemporal uninterrupted soil monitoring and soil management execution. First, the barriers for reliable RTCSM data acquisition are elucidated by examining typical soil monitoring techniques (e.g., electrochemical and spectroscopic sensors). Next, the prevailing challenges of the RTCSM sensor network, data transmission, data processing, and personalized data management are comprehensively discussed. Furthermore, this review explores RTCSM data application for updating diverse strategies including high-fidelity soil process models, control methodologies, digital soil mapping, soil degradation, food security, and climate change mitigation. Finally, the significance of RTCSM implementation in agricultural and environmental fields is underscored through illuminating future directions and perspectives in this systematic review.