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The grid-forming inverter (GFM) is widely acknowledged for its capabilities of forming both grid frequency and voltage. This letter investigates an extension of GFM, which decouples the capabilities of forming grid frequency and voltage. Based on the GFM inverter with matching control, a virtual resistor in series with the dc-link capacitor is added, so that the inverter can stably and quickly respond to outer grid frequency variations [i.e., frequency-following (FFL)], and meanwhile enhance the voltage stiffness of power grids [i.e., voltage-forming (VFM)]. The dc-link voltage regulation is also integrated into the synchronization control, making this inverter fit for short-term constant power inputs, such as renewable energy resources. A full-order state-space model is established to assess the stability of the inverter. Control parameters are properly tuned. A 3 kW grid-tided inverter experiment platform is tested and a modified IEEE 68-bus power system is simulated to validate the decoupled FFL and VFM features of the inverter.

GaInSn liquid metal is demonstrated to be efficient for resolving the adsorption of polysulfides and subsequent catalytic conversion of Na2S in the metal sulfide anode for Na-ion batteries.

In response to the threat to societal security posed by unauthorized unmanned aerial vehicles (UAVs), there is an urgent need for a fast and accurate method of identifying UAVs. The detection and identification of UAVs based on radio frequency (RF) fingerprint features are considered crucial for addressing this issue. This article presents a model for identifying micro-UAV targets based on RF fingerprint information. The system aims to effectively address the challenges of UAV safety management by providing reliable support for UAV control and management. In this model, based on the collected UAV RF signals in the environment, the six statistical RF features of instantaneous amplitude and fractal dimension (FD) in the time-domain information are then extracted, resulting in the combined 7-D RF fingerprints of the UAV. The 7-D RF fingerprints are then reduced to three dimensions using the t-distributed stochastic neighbor embedding (t-SNE) and principal component analysis (PCA) methods. Finally, the reduced dimensional data are fed into three machine learning algorithms: support vector machine (SVM), weighted K-nearest neighbor (WKNN), and random forest (RandF). The identification results are as follows: in the self-constructed UAV RF dataset, the median identification accuracy using SVM, WKNN, and RandF is up to 98.06%, 99.64%, and 99.63%, respectively. In the open-source UAV RF dataset, the median identification accuracy for SVM, WKNN, and RandF is up to 95.33%, 98.91%, and 98.91%, respectively. All the above experimental results confirm the reliability and effectiveness of the proposed model described in this article.

Modular multiactive-bridge (MMAB) converter is increasingly recognized as a potential solution for efficient power conversion in the integration of distributed energy resources, storage systems, and loads. However, optimizing MMAB converters for high-efficiency operation remains a challenge due to the inherent coupling across ports. To address this problem, this article proposes an inductance-current-minimization optimization scheme based on a hardware decoupling method. The decoupling across ports is achieved by eliminating the inductance at one of the ports, which simplifies the complexity of the system significantly. Based on the decoupled MMAB converter, an optimization scheme is proposed to minimize the root mean square value of the inductor current. Compared with the conventional modulation scheme, this scheme can reduce the inductor current and expand the soft-switching region across various power ranges and voltage ratios, thereby enhancing the operation efficiency. Furthermore, the proposed scheme is based on analytical solutions, enabling online implementation without the need for look-up tables and scalable to accommodate any number of ports. The effectiveness of the proposed scheme has been verified by a series of simulations and experiments based on a four-port MMAB converter prototype.

The high and growing penetration of inverter-based resources (IBR) in power systems challenges the way that system strength is assessed. It has been noticed that the standard indicator of system strength, short-circuit ratio (SCR), is not fully effective in anticipating the sub/super-synchronous oscillation phenomena that can arise from interactions of the control system of an IBR and the rest of the system. In this paper, system strength is first viewed from two perspectives, small-signal system strength and large-signal system strength, which is a natural separation considering the markedly different response of an IBR in normal operation and under faults. For assessing the onset of small-signal instability, a new metric for small-signal system strength is proposed and named Impedance Margin Ratio (IMR). IMR is the ratio between the allowed variation of the impedance of an IBR and the original value of the impedance at a particular point of interconnection. IMR is assessed at the oscillatory frequency of the dominant mode or taken as the minimum value across several modes. It is shown that as a localised frequency-domain indicator, IMR can successfully reveal weak points in the system towards various causes of oscillations.

This article introduces the single power-conversion resonant dc/ac converter, which eliminates the requirement for an output filter. Use of a resonant neutral-point-clamped cell allows nearly zero-voltage-switching turn-off of the primary-side outer switches at high instantaneous power, and close to half of the input voltage at low instantaneous power. Through the use of split transformer and semibridgeless structure with the secondary-side duty-ratio fixed at 0.5, the converter ensures a ripple-free output current, negating the need for an additional output filter. Notably, this topology also circumvents magnetizing current flow through the external resonant inductor, thereby, reducing the core size of the resonant inductor. In addition, the design facilitates zero current switching turn-off at the primary-side inner switches and effectively prevents any reverse recovery problem with the primary-side diodes. To validate the proposed concept, a 1-kW rated prototype capable of converting 400-V input to a 220-V$_{\text{rms}}$ grid voltage was constructed and experimentally tested.

Hsp70-Bim protein–protein interaction (PPI) is the most recently identified specific target in chronic myeloid leukemia (CML) therapy. Herein, we developed a new class of Hsp70-Bim PPI inhibitors via scaffold hopping of S1g-10, the most potent Hsp70-Bim PPI inhibitor thus far. Through structure–activity relationship (SAR) study, we obtained a biphenyl scaffold compound JL-15with a 5.6-fold improvement in Hsp70-Bim PPI suppression (Kd= 123 vs 688 nM) and a 4-fold improvement in water solubility (29.42 vs 7.19 μg/mL) compared to S1g-10. It maintains comparable apoptosis induction capability with S1g-10against both TKI-sensitive and TKI-resistant CML cell lines in an Hsp70-Bim-dependent manner. Additionally, through SAR, 1H–15N TRSOY-NMR, and molecular docking, we revealed that Lys319 is a “hot spot” in the Hsp70-Bim PPI interface. Collectively, these results provide a novel chemical scaffold and structural insights for the rational design of Hsp70-Bim PPI inhibitors.

Background: Analyzing fundus images with deep learning techniques is promising for screening systematic diseases. However, the quality of the rapidly increasing number of studies was variable and lacked systematic evaluation. Objective: To systematically review all the articles that aimed to predict systemic parameters and conditions using fundus image and deep learning, assessing their performance, and providing suggestions that would enable translation into clinical practice. Methods: Two major electronic databases (MEDLINE and EMBASE) were searched until August 22, 2023, with keywords ‘deep learning’ and ‘fundus’. Studies using deep learning and fundus images to predict systematic parameters were included, and assessed in four aspects: study characteristics, transparent reporting, risk of bias, and clinical availability. Transparent reporting was assessed by the TRIPOD statement, while the risk of bias was assessed by PROBAST. Results: 4969 articles were identified through systematic research. Thirty-one articles were included in the review. A variety of vascular and non-vascular diseases can be predicted by fundus images, including diabetes and related diseases (19%), sex (22%) and age (19%). Most of the studies focused on developed countries. The models’ reporting was insufficient in determining sample size and missing data treatment according to the TRIPOD. Full access to datasets and code was also under-reported. 1/31(3.2%) study was classified as having a low risk of bias overall, whereas 30/31(96.8%) were classified as having a high risk of bias according to the PROBAST. 5/31(16.1%) of studies used prospective external validation cohorts. Only two (6.4%) described the study’s calibration. The number of publications by year increased significantly from 2018 to 2023. However, only two models (6.5%) were applied to the device, and no model has been applied in clinical. Conclusion: Deep learning fundus images have shown great potential in predicting systematic conditions in clinical situations. Further work needs to be done to improve the methodology and clinical application.

Due to the complex thermal runaway propagation behavior of lithium-ion battery modules and the generation of a substantial amount of combustible mixed gases during the process, their safety performance is compromised, hindering their large-scale application in the fields of energy storage and automotive industries. Therefore, in this paper, the thermal runaway propagation behavior and gas production characteristics of Li (Ni0.6Co0.2Mn0.2) O2battery modules with different state of charge were investigated in closed space. The results show that no thermal runaway occurred in the battery module when the state of charge was below 25% in closed space. With the decrease of the state of charge, both the internal thermal propagation speed and the surface temperature of the lithium-ion battery were reduced. The total gas production volume from 17.48 mol to 6.17 mol when the state of charge was reduced from 100% to 50%. The main gases produced during thermal runaway were CO2, CO, H2, CH4, and C2H4. With the decrease of the state of charge, the content of CO2increased, the content of C2H4and CO decreased, and the content of H2and CH4remained unchanged with the state of charge. The results contribute to safety measures for preventing thermal runaway accidents in lithium-ion batteries, offering guidance for the secure design of lithium-ion battery packs, and thus enhancing the overall safety of lithium-ion batteries.

A growing trend in the field of adversarial attacks is evolving from the digital domain to the more challenging physical domain. The previous works mainly employ printable adversarial patches with special textures in real-world physical attacks. However, due to lighting conditions and atmospheric scattering, the texture-based patches are prone to distortion in the long-range situation than in the close-range case, resulting in poor physical attack performance in remote sensing scenarios. Therefore, this article proposes a new physical attack method using single-color strip-based patches to hide the objects from being detected correctly in optical aerial detection. Specifically, we design a differentiable representation and an optimization method to optimize the position, thickness, and color of the adversarial strips. Compared with the traditional complex texture-based patch, the proposed strip-based patch is more robust when mapping from the digital domain to the physical domain. Extensive experiments are conducted on multiple datasets and real-world scenarios to evaluate the attack performance of various attack methods. The results show that the proposed strip-based adversarial patch has better attack performance against white-box, black-box, and even defense detectors. Furthermore, we can improve the physical attack success rate (ASR) in remote sensing scenarios by about 70% compared with previous texture-based methods.

Aphids and mites, which cause severe yield reductions, are commonly controlled by commercial insecticides, which has led to serious environmental problems. Research is currently being performed on developing safe and low-toxicity botanical pesticides, which are an effective way to reduce environmental pollution and pesticide residue in food. Trifolium pratenseL. is a globally important forage and vulnerable to aphids and mites. The differences in secondary metabolites between infested leaves (ILs) and pest-free leaves (PFLs) of T. pratensewere investigated by gas chromatography–mass spectrometry (GC–MS) and high-performance liquid chromatography (HPLC) to determine the chemical defense mechanism. The results revealed significant differences in the contents of fatty acids and isoflavones. The contents of neophytadiene, methyl linoleate, daidzein, genistein, formononetin, and biochanin A in ILs were significantly increased compared with PFLs. The insecticidal compounds were further separated by bioassay-directed fractionation against Myzus persicaeSulzer, Brevicoryne brassicaeLinnaeus and Tetranychus cinnabarinusBoisduval. The ethyl acetate (EtOAc) extract had greater insecticidal activity than the petroleum ether (PE) extract. Notably, there was no significant difference between the EtOAc extract and avermectin at 1.00 mg/mL against T. cinnabarinusafter 48 h. The following ten compounds were obtained from the PE and EtOAc extracts: prunetin (1), genistein (2), formononetin (3), biochanin A (4), 8-hydroxy-4',7-dimethoxyisoflavane (5), L-maackiain (6), 4-hydroxy-3-methoxy-(6aR,11aR)-8,9-methylenedioxypterocarpan (7), β-sitosterol (8), linoleic acid (9) and linoleic acid monoglyceride (10). Compound 7exhibited the highest level of insecticidal activity, which was close to that of avermectin. The results indicated that isoflavones were the main active insecticidal components.

Cathodes with high cycling stability and rate capability are required for ambient temperature sodium ion batteries in renewable energy storage application. Na3V2(PO4)3is an attractive cathode material with excellent electrochemical stability and fast ion diffusion coefficient within the 3D NASICON structure. Nevertheless, the practical application of Na3V2(PO4)3is seriously hindered by its intrinsically poor electronic conductivity. Herein, solvent evaporation method is presented to obtain the nitrogen-doped carbon coated Na3V2(PO4)3cathode material, delivering enhanced electrochemical performances. N-Doped carbon layer coating serves as a highly conducting pathway, and creates numerous extrinsic defects and active sites, which can facilitate the storage and diffusion of Na+. Moreover, the N-doped carbon layer can provide a stable framework to accommodate the agglomeration of the electrode upon electrode cycling. N-Doped carbon coated Na3V2(PO4)3(NC-NVP) exhibits excellent long cycling life and superior rate performances than bare Na3V2(PO4)3without carbon coating. NC-NVP delivers a stable capacity of 95.9 mA·h/g after 500 cycles at 1 C rate, which corresponds to high capacity retention(94.6%) with respect to the initial capacity(101.4 mA·h/g). Over 91.3% of the initial capacity is retained after 500 cycles at 5 C, and the capacity can reach 85 mA·h/g at 30 C rate.

Participation analysis based on whole-system impedance models enables the root-cause stability analysis of large-scale power systems when state-space models are unavailable. To extend participation analysis to the hybrid AC/DC system, especially for the interlinking converter, this article proposes two complementary methods, namely the direct method and the indirect method. The direct method treats the interlinking converter as hybrid AC-DC port apparatus and the participation factor can be defined with the terminal hybrid AC-DC impedance matrix correspondingly. As for the indirect method, system equivalence can be made at the AC or DC terminal of the interlinking converter when impedance models within subsystems are unavailable such that preliminary knowledge of systems can be simplified. Importantly, the equivalence of these two methods on the observability of system components is revealed through theoretical analysis. The proposed impedance participation analysis methods are illustrated with a 4-bus hybrid AC/DC system, a modified 28-bus hybrid AC/DC system and a modified 96-bus hybrid AC/DC system. Numerical calculations and time-domain simulations are performed to validate the theoretical analysis.

State space is widely used for modeling power systems and analyzing their dynamics but it is limited to representing causal and proper systems in which the number of zeros does not exceed the number of poles. In other words, the system input, output, and state can not be freely selected. This limits how flexibly models are constructed, and in some circumstances, can introduce errors because of the addition of virtual elements in order to connect the mismatched ports of subsystem models. An extension known as descriptor state space (also known as implicit state space, generalized state space, singular state space) can model both proper and improper systems and is a promising candidate for solving the noted problems. It facilitates a modular construction of power system models with flexible choice of ports of subsystems. Algorithms for mathematical manipulation of descriptor state space models are derived such as preforming inverse, connection, and transform. Corresponding physical interpretations are also given. Importantly, the proposed algorithms preserve the subsystem states in the whole system model, which therefore enables the analysis of root causes of instability and mode participation. Theoretical advances are validated by example power systems of varied scales including inductor or capacitor systems, and modified IEEE 14-bus, 68-bus, and 118-bus generator-inverter-composite systems.

This article investigates the impact of frame transformations on the accuracy of numerical discretization in power system transient and stability studies. As analysed, frame transformations influence the convergence of the numerical discretization. Specifically, for an explicit discretization method (e.g., forward Euler method), the stability of the original system is best preserved in the frame where the system eigenvalue is closer to the origin of the complex plane, e.g., in the stationary frame for inductors and capacitors, and in the synchronous frame for dq-frame controllers of inverters. Simulation results are given to validate the theoretical analysis.

The application of Explainable Artificial Intelligence (XAI) in the field of chemistry has garnered growing interest for its potential to justify the prediction of black-box machine learning models and provide actionable insights. We first survey a range of XAI techniques adapted for chemical applications and categorize them based on the technical details of each methodology. We then present a few case studies to illustrate the practical utility of XAI, such as identifying carcinogenic molecules and guiding molecular optimizations, in order to provide chemists with concrete examples of ways to take full advantage of XAI-augmented machine learning for chemistry. Despite the initial success of XAI in chemistry, we still face the challenges of developing more reliable explanations, assuring robustness against adversarial actions, and customizing the explanation for different applications and needs of the diverse scientific community. Finally, we discuss the emerging role of large language models like GPT in generating natural language explanations and discusses the specific challenges associated with them. We advocate that addressing the aforementioned challenges and actively embracing new techniques may contribute to establishing machine learning as an indispensable technique for chemistry in this digital era.

The present study clarified the forest‐age associated change in the pericarp quality of Zanthoxylum planispinum‘dintanensis’ (ZPD) and aimed to provide a theoretical foundation for formulating management strategies. The studied forest age groups consisted of 5‐to‐7‐, 10‐to‐12‐, 20‐to‐22‐, and 28‐to‐32‐yr‐old ZPD plants growing in rocky desertified areas of karst dry‐hot valleys in Guizhou, China. By measuring the pericarp quality indices of the plants present in the stands, the changes in pericarp quality were determined. The quality traits were optimal when the forest age was 28–32 yr, and the quality did not degrade with increased forest age. The sensitivity levels of ash contents and vitamins to the forest age were weak, whereas the levels of amino acids and trace elements were more affected by forest age. The latter two compounds were, therefore, more suitable for quality grading. The comprehensive quality index of the 28‐to‐32‐yr‐old plant group was the highest, followed by the 10‐to‐12‐yr‐old, 5‐to‐7‐, and 20‐to‐22‐yr‐old plants. Pericarp quality of the 28‐to‐32‐yr‐old plant group was the best and did not degenerate as the forest aged.The variations in amino acids and trace elements levels were high.The fruit cultivation objectives differed along with age.

The large-scale integration of converter-interfaced resources in electrical power systems raises new threats to stability which call for a new theoretical framework for modelling and analysis. In this paper, we present the intrinsic analogy of a power system to a communication system, which is here called power-communication isomorphism. Based on this isomorphism, we revisit power system stability from a communication perspective and thereby establish a theory that unifies the heterogeneous power apparatuses of power systems and provides a bridge between electromagnetic transient (EMT) and phasor dynamics. This theory yields several new insights into power system stability and new possibilities for stabilization. In particular, we demonstrate that a system of 100% converter-interfaced resources can achieve stable synchronization in small- and large-signal sense under grid-following control which was commonly considered impossible.

Low‐intensity ultrasound (LI‐US) is a non‐invasive stimulation technique that has emerged in recent years and has been shown to have positive effects on neuromodulation, fracture healing, inflammation improvement, and metabolic regulation. This study reports the conclusions of a bibliometric analysis of LI‐US. Input data for the period between 1995 and 2022, including 7209 related articles in the field of LI‐US, were collected from the core library of the Web of Science (WOS) database. Using these data, a set of bibliometric indicators was obtained to gain knowledge on different aspects: global production, research areas, and sources analysis, contributions of countries and institutions, author analysis, citation analysis, and keyword analysis. This study combined the data analysis capabilities provided by the WOS database, making use of two bibliometric software tools: R software and VOS viewer to achieve analysis and data exploration visualization, and predicted the further development trends of LI‐US. It turns out that the United States and China are co‐leaders while Zhang ZG is the most significant author in LI‐US. In the future, the hot spots of LI‐US will continue to focus on parameter research, mechanism discussion, safety regulations, and neuromodulation applications.

We report the discovery of a new imine reductase (IRED), named AtIRED, by genome mining. Site-saturation mutagenesis on AtIRED generated two single mutants M118′L and P120′G and the double mutant M118′L/P120′G with improved specific activity toward sterically hindered 1-substituted dihydro-β-carbolines. The synthetic potential of these engineered IREDs was showcased by the preparative-scale synthesis of nine chiral 1-substituted tetrahydro-β-carbolines (THβCs), including (S)-1-t-butyl-THβC and (S)-1-t-pentyl-THβC, in 30–87% isolated yields with excellent optical purities (98–99% ee).

This work examines the palladium(ii) catalysed oxidation of terminal alkenes to their corresponding methyl ketones using tert-butyl hydroperoxide (TBHP) as the oxidant. The study aimed to reduce catalyst loadings and to understand some of the factors which are important in the design of more effective methods. A series of ligands based around 2-(2-pyridyl)benzoxazole (PBO) were studied and a new dicationic catalyst was developed which can operate more efficiently than previously reported catalysts. The choice of solvent system was also found to have a significant impact on catalyst performance. In the case of oct-1-en-3-yl acetate, a model substrate for a challenging class of substrates (protected allylic alcohols), it was found that using 1,1,1,3,3,3-hexafluoroisopropanol (HFIP), as part of a solvent mixture, greatly improved the reaction; enabling shorter reaction times and lower catalyst loadings.

Impedance models of power systems are useful when state-space models of apparatus such as inverter-based resources (IBRs) have not been made available and instead only black-box impedance models are available. For tracing the root causes of poor damping and tuning modes of the system, the sensitivity of the modes to components and parameters are needed. The so-called critical admittance-eigenvalue sensitivity based on nodal admittance model has provided a partial solution but omits meaningful directional information. The alternative whole-system impedance model yields participation factors of shunt-connected apparatus with directional information that allows separate tuning for damping and frequency, yet do not cover series-connected components. This paper formalises the relationships between the two forms of impedance models and between the two forms of root-cause analysis. The calculation of system eigenvalue sensitivity in impedance models is further developed, which fills the gaps of previous research and establishes a complete theory of impedance-based root-cause analysis. The theoretical relationships and the tuning of parameters have been illustrated with a three-node passive network, a modified IEEE 14-bus network and a modified NETS-NYPS 68-bus network, showing that tools can be developed for tuning of IBR-rich power systems where only black-box impedance models are available.

Ferulic acid (FA) is one of the most abundant bound phenolics in whole grains, partly contributing to its preventive effects on metabolic syndrome (MetS). The study aims to investigate if FA mediates MetS through the regulation of hepatic metabolisms and the insulin receptor related pathways in the palmitate-treated HepG2 cells (MetS model). We found that FA (50, 100, and 200 μM) dramatically ameliorated the lipid accumulation in the MetS model. FA significantly decreased the activities of the gluconeogenic enzymes, G6Pase and PEPCK, downregulated the lipogenic enzyme FAS-1, and upregulated the lipolytic enzyme CPT-1 by regulating a series of transcriptional factors including HNF4α, FOXO-1, SREBP-1c, and PPAR-γ. Notably, we found that FA’s ability to alleviate MetS is achieved by activating the insulin receptor/PI3K/AKT pathway. Our results validated the effects of FA on mediating the metabolic disorders of lipid and glucose pathways and unveiled its potential intracellular mechanisms for the prevention of MetS.

In this study, a zeolite-doped PbO2(Zeo-PbO2) anode was fabricated via electrodeposition technology for electrochemical oxidation of m-nitrophenol (m-NP). The results of characterization revealed that doping zeolite efficiently promoted the oxygen evolution potential and active sites of PbO2anode, thereby higher performance to generate •OH radical and degrade the m-NP. The •OH radical was confirmed to be the primary contributor to the electrochemical oxidation of m-NP by scavenging tests. Thus, the effect mechanism of current density, supporting electrolyte, and initial pH on the removal of m-NP in the electrochemical oxidation process was explored by the generation of •OH radicals at different conditions. Based on the vulnerable sites of m-NP to the attack of •OH radical supposed by the theoretical calculation and degradation intermediates identified by GC-MS and IC, the degradation pathways of m-NP in the electrochemical oxidation process were speculated. Besides, the good recyclability and stability of Zeo-PbO2anode were exhibited in consecutive m-NP degradation, •OH generation experiments, and accelerated life tests.

In this work, field control experiments were conducted to clarify the effects of three management practices (dwarf and close planting + no sprinkling irrigation (A1), dwarf and close planting + sprinkling irrigation (A2), and natural growth (CK) on the quality of pericarp of Zanthoxylum planispinum‘dintanensis’ in a plantation setting in a rocky desertification area of Central Guizhou Province. The results showed that the ash concentration of A2was the highest (6.70% ± 0.42%), yet there was no significant difference from A1(6.60% ± 0.14%) and CK (6.65% ± 0.64%), and methionine and cystine were not detected (detection limits were 13.90 and 43.60 mg kg–1, respectively). A2management could significantly promote the concentration of amino acids (P < .05); vitamin C was not detected (detection limit was 0.20 mg kg–1), vitamin E ranked as A2> A1> CK, and β‐carotene ranked as A1> A2> CK. There were significant differences among management treatments; A1, A2, and CK had no significant effect on trace elements. In conclusion, dwarf and close planting + sprinkling irrigation is an ideal management practice improving the quality of dintanensis. Effects of ecological management practices on quality of Zanthoxylum planispinum‘dintanensis’.Dwarf and close planting + sprinkling irrigation could significantly promote the concentration of amino acids.Dwarf and close planting + sprinkling irrigation improves dintanensis quality.

Visual strain sensors have attracted significant attention in the smart wearables field due to their ability to intuitively monitor human health and movement through color displays. However, their development is limited by restricted sensitivity over a large strain range and poor cyclic stability. The conductivity and electricity-induced heating performance can be enhanced by filling microcracks with conductive nanoparticles to increase the conductive paths. Inspired by this concept, nanocarbon powder/silver nanoparticles@carboxylic multi-walled carbon nanotubes/polyurethane (NAMP) fiber-based visual strain sensors were developed using wet spinning, in-situ polymerization, and ultrasonic impregnation techniques. The NAMP sensors exhibit high sensitivity (GF=3528), a wide strain range (0–107 %), and excellent cyclic stability (over 5000 cycles), demonstrating high reusability, stability, and durability. Meanwhile, a wide temperature range from 27.8 °C to 75.2 °C and corresponding color display changes triggered by applied voltages from 0 to 2.5 V were achieved, indicating excellent visualization performance. In addition, the integration of NAMP fibers into temperature-adjustable electrothermal fabric can be utilized for human thermal management therapy and deicing. This work provides valuable insights into the design and potential applications of intelligent fibrous sensor, paving the way for the development of wearable textiles from fibers to fabrics.

The solid-liquid contact electrification (CE) has been widely studied due to its importance in diverse fields. However, the evidence indicates that the relevant charge transfer or the charge accumulation in the aqueous droplets might start prior to the solid-liquid contact. Nevertheless, this pre-contact electrification of the aqueous droplets is still rarely studied. Here, the effect of a charged FEP (Fluorinated ethylene propylene) hydrophobic film on the water droplets falling from a syringe needle was studied systematically. It was found that the aqueous droplets can be charged differently in the vicinity of the FEP film, shifting gradually from positively to negatively charged. Based on the kinetic analysis of the droplet falling motion and the numerical simulation of the electrostatic field in the vicinity of the FEP film, we propose a charge-transfer mechanism for this pre-contact electrification of the water droplets, which involves a competition between the positive-charge generation caused by the detachment of the aqueous droplets under the electrostatic field and the capture of the negative-charge jets by the aqueous droplets. This competitive mechanism, verified by both the aperture limiting effect and the FEP-surface-potential dependence of the negative charge jets, allows for a flexible control over the charge polarity and charge amount of the falling water droplets. The outcome of this study expands the solid-liquid charge-transfer mechanism and broadens the application scope of the aqueous droplet charging.

In this paper, we investigate the problem of command attacker synthesis in cyber–physical systems at the supervisory control layer. The attacker attempts to inflict damage or reach a blocking state, while undetected by the supervisor. Specially, we propose the so-called Command Attack Structure (CAS) to capture attack processes and describe the interaction between the attacker and the supervisory control system. To address the races between attack events and commands or internal events of the control system during the interaction, we propose the conditions of strictly race-free and loosely race-free, which the attacker must adhere to. Moreover, we design pruning algorithms to achieve covert and race-free CASs, and ultimately obtain successful command attackers. Lastly, we also consider combined attacks, which involve multiple attack operations on a single command. Then the combined CAS is designed to model and analyze such scenarios. • Propose a unique game-like structure, the Command Attack Structure (CAS), to describe the specific attack processes. • Investigate the race between inserted events and system events. • Design pruning algorithms to restrict the actions of the attacker. • Extend the single attack operations to multiple attack operations for a single command. [ABSTRACT FROM AUTHOR]

By innovatively coupling human body and functional fibers, recent inspiring research achieves radiation energy capture, signal transmission and digital visualization while retains flexibilities, softness and durability of textiles and their large‐scale fabrications. The single coupled human‐fiber system provides absolutely new mechanism and fibrous method to design and produce intelligent textiles and promises to offer more potential applications in home and daily lives.

In this paper, we present a 3D multimodal router for aqueous microdroplets, addressing the ongoing challenge of 3D transportation of microdroplets in free space. Our strategy combines the photovoltaic effect of a LiNbO3:Fe substrate with the triboelectric effect of a superhydrophobic coating. We demonstrate that by positively charging the aqueous microdroplets through the contact-electrification process while they roll or elongate on the superhydrophobic surface, we can achieve their controlled and electrostatically ballistic ejection for free-space transportation. Leveraging the superfast and repetitive laser-scanning capabilities of a laser marking system, we develop various 2D/3D routing modes that utilize repulsive or attractive electrostatic forces to deflect or trap the microdroplets in parallel or cascading configurations. We propose a 3D routing mode suitable for photosensitive microdroplets, employing asynchronous illumination with an appropriate ON/OFF duty ratio. We also enable the 3D omnidirectional routing of initially motionless and traveling microdroplets using toroidal and linear laser-scanning, respectively, thus facilitating the transportation of sequential microdroplets in overpass configurations. Our proposed technique opens up new possibilities for free-space microfluidics, enabling the complex delivery of biochemical substance.

As the latest technology to promote the transformation of the new energy automobile industry, cell-to-pack (CTP) batteries have attracted unprecedented attention. However, there is a lack of research on the vented gases at the module level. In this paper, the thermal runaway propagation (TRP) behavior and vented gas characteristics of large modules of cell-to-pack batteries in closed space are studied experimentally. Results indicated that the total quantity of vented gas from module during thermal runaway (TR) process was 31.01 mol. The main components of vented gas are H2, CO2, CO, C2H4, and CH4. Of these, H2, CO2and CO accounted for 32.85 %, 28.00 % and 25.25 % respectively. In addition, the calculated explosion limit of vented gases was 6.27 %–54.14 %. Therefore, the vented gas is prone to explosion, causing serious accidents. Compared with the open space system, the TRP temperature of the battery module in the closed nitrogen space system was higher and the mass loss was smaller. This study can provide reference for the prevention of TRP in large battery modules, and for evaluating the fire hazard of powered vehicles.

This article first considers the research problem of fairness in collaborative deep learning, while ensuring privacy. A novel reputation system is proposed through digital tokens and local credibility to ensure fairness, in combination with differential privacy to guarantee privacy. In particular, we build a fair and differentially private decentralised deep learning framework called FDPDDL, which enables parties to derive more accurate local models in a fair and private manner by using our developed two-stage scheme: during the initialisation stage, artificial samples generated by Differentially Private Generative Adversarial Network (DPGAN) are used to mutually benchmark the local credibility of each party and generate initial tokens; during the update stage, Differentially Private SGD (DPSGD) is used to facilitate collaborative privacy-preserving deep learning, and local credibility and tokens of each party are updated according to the quality and quantity of individually released gradients. Experimental results on benchmark datasets under three realistic settings demonstrate that FDPDDL achieves high fairness, yields comparable accuracy to the centralised and distributed frameworks, and delivers better accuracy than the standalone framework.

Carbon dot is a type of carbon material with an ultrasmall size of less than 10 nm for all three dimensions, which has attracted more and more attention due to its useful merits. Unfortunately, the complicated synthesis method and low yield largely limit its wide large-scale application. Herein, an inexpensive and high-efficiency aldol condensation method under ambient temperature and pressure was proposed for the large-scale synthesis of CDs, which can obtain products with 1.083 kg in 2 h and realize the functionalization of carbon dots doped with nitrogen (NCDs) and sulfur/nitrogen doubly (NSCDs), and then the mechanism and structure of CDs formation were explained. Moreover, utilizing the feature of controllable assembly of carbon dots, and combined with theoretical calculations, we have designed functionalized 1D carbon fibers (CF) to construct high-performance potassium storage anode materials through the assembly of carbon dots induced by a Zn compound. Benefitting from the microstructure and surface functional groups derived from CDs, the N-doped CF (NCF700) exhibits superior electrochemical energy storage performance for potassium ion batteries (PIBs). This study provides a low-cost and high-yield method to produce CDs and promotes the practical application of CDs in electrochemical energy storage.

Zanthoxylum planispinumvar. dintanensisis an ecological and economic tree species widely planted in the canyon area of the Guizhou Karst Plateau. However, the rapid growth decline of its plantation in large scale has become one of the major environmental problems affecting vegetation restoration and the consolidation of the achievements of rocky desertification control. In this study, we collected soil samples in the full bloom period (March) and the vigorous growth period (June) from the declining and normal‐growing Z. planispinumvar. dintanensiscommunities. Through analyzing fertility quality indexes such as water content and nutrient levels, the mechanism of water–fertilizer coupling affecting the growth decline of Z. planispinumvar. dintanensisplantation was explored. The results showed that the pH of soil was about 7.0, and there was no significant difference between the two types of communities. The surface soil water content of the declining community was <20%, which was significantly lower than that of the normal‐growing Z. planispinumvar. dintanensiscommunity. The deficiency of quick‐acting nutrients (e.g., available Ca, Mg, and B) and the imbalance of the medium and trace elements could cause the growth decline of Z. planispinumvar. dintanensis, indicating that we should pay attention to the addition of quick‐acting nutrients and their stoichiometry in the management process of Z. planispinumvar. dintanensis. The comprehensive analysis showed that the decrease of soil water content induced the water–fertilizer coupling deficit effect, which might be one of the reasons for the growth decline of the Z. planispinumvar. dintanensiscommunity, in view of the fact that there are many factors causing the growth decline of artificial forest and the mechanism is complex. In the future, it is necessary to systematically study the growth decline of Z. planispinumvar. dintanensiscommunity from the perspectives of environmental media, root exudates, plant endogenous hormones, soil microorganisms, and water–fertilizer coupling based on the variations in time and space.

Weak electrocatalytic activity of the La0.8Sr0.2MnO3−δ(LSM) oxygen electrode at medium and low temperatures leads to decreasing performance both in the solid oxide fuel cell (SOFC) mode and the solid oxide electrolysis cell (SOEC) mode. Herein, we design an Er0.4Bi1.6O3−δ(ESB) functionalized La0.8Sr0.2MnO3−δ(labeled as LSM/ESB) oxygen electrode via a one-step co-synthesis modified Pechini method. The unique LSM/ESB with polarization resistance of only 0.029 Ω·cm2at 750 °C enables a highly enhanced rate of oxygen reduction and evolution reaction. The single cell with the LSM/ESB electrode achieves a maximum power density of 1.747 W cm–2at 750 °C, 2.6 times higher than that of the mechanically mixed LSM–ESB electrode (0.667 W cm–2). In the SOEC mode, it also shows the improved performance of the LSM/ESB electrode. Furthermore, the cell exhibits admirable durability of 90 h in the fuel cell mode and excellent reversibility. The better performance can be concluded as a better surface-active state and a tighter connection between the LSM and ESB particles of LSM/ESB via a co-synthesis process. This work proposes a novel strategy to advance the application of the one-step modified Pechini technology for an efficient and stable oxygen electrode.

Antenatal obesity increases the risk of postpartum depression. Previous research found that dietary fiber supplementation could alleviate mental behavioral disorders. The present study aims to uncover the effects of high-dietary fiber intake on high-fat diet (HFD)-induced depressive-like behaviors and its underlying mechanism. Female C57BL6/J mice were fed with HFD to establish an antenatal obese model. A high-dietary fiber intake (inulin, 0.037 g/kcal) significantly attenuated cognitive deficits and depressive-like behaviors in the maternal mice after the offspring weaning. High-dietary fiber intake upregulated the expression of 5-hydroxytryptamine (5-HT) and norepinephrine (NE) and suppressed neuroinflammation. Furthermore, high-dietary fiber intake restructured the gut microbiome and elevated the formation of short-chain fatty acids (SCFAs). Correlation analysis indicated that the increase in microbes such as Lactobacillusand S24-7, and SCFAs’ levels were positively correlated with behavioral improvements. In conclusion, high-dietary fiber intake is a promising nutritional intervention strategy to prevent antenatal obesity-induced behavioral disorders via a microbiota–gut–brain axis.

Analysis of ac electrical systems can be performed via frame transformations in the time-domain or via harmonic transfer functions (HTFs) in the frequency-domain. The two approaches each have unique advantages but are hard to reconcile because the coupling effect in the frequency-domain leads to infinite dimensional HTF matrices that need to be truncated. This paper explores the relation between the two representations and shows that applying a frame transformation on the input-output signals creates a direct equivalence to a similarity transformation to the HTF matrix of the system. Under certain conditions, such similarity transformations have a diagonalizing effect which, essentially, reduces the HTF matrix order from infinity to two or one, making the matrix tractable mathematically without truncation or approximation. This theory is applied to a droop-controlled voltage source inverter as an illustrative example. A stability criterion is derived in the frequency-domain which agrees with the conventional state-space model but offers greater insights into the mechanism of instability in terms of the negative damping (non-passivity) under droop control. Therefore, the paper not only establishes a unified view in theory but also offers an effective practical tool for stability assessment. [ABSTRACT FROM AUTHOR]

To study the Chinese human milk N-glycome over lactation, N-glycans were released and separated from serum proteins, purified by solid-phase extraction, and analyzed by matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF-MS). In total, 66 different putative N-glycans were found in the colostrum (week 1) and mature milk (week 4) of seven Chinese mothers. A clear difference was observed between milk of five secretor and two nonsecretor mothers, based on the type and relative amounts of the individual N-glycans. The relative levels of the total neutral nonfucosylated and the fucosylated N-glycans in milk of five secretor mothers increased and decreased over lactation, respectively. This pattern could not be observed for the milk from the two nonsecretor mothers. Overall, this was the first study that provided detailed information on individual N-glycans in milk among mothers and over time as well as that fucosylation of N-glycans in milk was associated with the mother’s secretor status.

Space life science has been a frontier discipline in the life sciences, aiming to study the life phenomena of earth organisms and their activity patterns under the special environment of space. This review summarizes studies in various key topics in space life science, namely, how microbiome changes in humans and plants, the development of space agriculture and the use of animal, plant and cell models to study the effect of space environments on physiology. We highlight the new possibilities of using high-quality protein crystals uniquely available when grown under space conditions to aid drug development on earth, and the state-of-the-art Bioregenerative Life Support Systems (BLSS) to achieve long term human survival in space.

Raman spectroscopy has been widely used for label-free biomolecular analysis of cells and tissues for pathological diagnosis in vitro and in vivo. AI technology facilitates disease diagnosis based on Raman spectroscopy, including machine learning (PCA and SVM), manifold learning (UMAP), and deep learning (ResNet and AlexNet). However, it is not clear how to optimize the appropriate AI classification model for different types of Raman spectral data. Here, we selected five representative Raman spectral data sets, including endometrial carcinoma, hepatoma extracellular vesicles, bacteria, melanoma cell, diabetic skin, with different characteristics regarding sample size, spectral data size, Raman shift range, tissue sites, Kullback–Leibler (KL) divergence, and significant Raman shifts (i.e., wavenumbers with significant differences between groups), to explore the performance of different AI models (e.g., PCA–SVM, SVM, UMAP–SVM, ResNet or AlexNet). For data set of large spectral data size, Resnet performed better than PCA-SVM and UMAP. By building data characteristic-assisted AI classification model, we optimized the network parameters (e.g., principal components, activation function, and loss function) of AI model based on data size and KL divergence etc. The accuracy improved from 85.1 to 94.6% for endometrial carcinoma grading, from 77.1 to 90.7% for hepatoma extracellular vesicles detection, from 89.3 to 99.7% for melanoma cell detection, from 88.1 to 97.9% for bacterial identification, from 53.7 to 85.5% for diabetic skin screening, and mean time expense of 5 s.

CO2electrolysis using solid oxide electrolysis cells is a promising technology for CO2utilization and conversion, which has attracted more and more attention in recent years because of its extremely high efficiency. However, traditional Ni-yttria-stabilized zirconia (Ni-YSZ) or Ni–Gd0.1Ce0.9O2−δ(Ni-GDC) metal-ceramic cathode faces many problems such as Ni agglomeration and carbon deposition during long-time operation. Herein, a perovskite oxide La0.43−xCa0.37Ti0.9Ni0.1O3−δ(LCTN, x = 0, 0.05, 0.1) with nanophase-LaVO4exsolution was investigated as the novel cathode of solid oxide electrolysis cell (SOEC) for efficient CO2electrolysis. The results confirm that the exsolution nanophase on LCTN surface can significantly improve the CO2adsorption and conversion performance. For CO2electrolysis at 1.8 V, an electrolysis current density of 1.24 A/cm2at 800 °C can be obtained on SOEC with La0.43−xCa0.37Ti0.9Ni0.1O3−δdecorated with LaVO4(LCTN-V0.05) cathode. Furthermore, the corresponding cell can maintain stable operation up to 100 h without apparent performance degradation. These results demonstrate that doping-induced second nanophase exsolution is a promising way to design high-performance SOEC cathodes for CO2electrolysis.

The synthesis and structural revision of the dimerized cyclic hexapeptides antatollamides A (1) and B (2) are reported. These are unique peptides with two proline residues and bicyclic peptides combined by a disulfide bond. Cyclization and disulfide bond formation of the linear peptide led to antatollamide A (1). However, the 1H and 13C NMR spectra of synthetic antatollamide A (1) were not consistent with those of isolated antatollamide A (1). Meanwhile, the NMR spectra of the monomeric cyclic hexapeptide cyclo(Pro-Pro-Phe-dCys-Ile-Val) (3) and the isolated antatollamide A (1) were identified completely. In addition, we found that isolated antatollamide B (2) is cyclo(Pro-Pro-dPhe-dCys-Ile-Val) (4).

This paper presents an effective approach for recognising human actions from depth video sequences by employing depth motion maps (DMMs) and convolutional neural networks (CNNs). Depth maps are projected onto three orthogonal planes, and frame differences under each view (front/side/top) are then accumulated through an entire depth video sequence generating a DMM. We build a model architecture of multi-view convolutional neural network (MV-CNN) containing multiple networks to deal with three DMMs (DMMf, DMMs, DMMt). The output of full-connected layer under each view is integrated as feature representation, which is then learned in the last softmax regression layer to predict human actions. Experimental results on MSR-Action3D dataset and UTD-MHAD dataset indicate that the proposed approach achieves state-of-the-art recognition performance and is appropriate for real-time recognition.

ω-3 polyunsaturated fatty acids, naturally found in fish oil, are highly desirable for their associated health benefits. However, they are highly prone to oxidation and degradation. We examined the feasibility of simultaneously adding a solid lipid (palmitic acid) and an antioxidant (quercetin) into a whey-protein-isolate-stabilized solid lipid nanoparticle emulsion for encapsulating fish oil. The goal was to find a rational and new formulation containing both solid lipid and antioxidant that can encapsulate fish oil and give it the best physicochemical stability. Our results show that adding palmitic acid improved the physical stability of the emulsions by decreasing the size of the oil-in-water droplets. On the basis of the thiobarbituric acid reactive substances assay, we found out that at low concentrations of palmitic acid the addition of quercetin played a dominant role in increasing the oxidation stability of fish oil. On the contrary, at high concentrations of palmitic acid, it was palmitic acid that dominated the oxidation inhibition by the solidification of the encapsulates’ core.