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ABSTRACT Background: Chemotherapy-induced peripheral neuropathy (CIPN) is a severe adverse reaction to chemotherapeutics, which seriously affects the outcome of chemotherapy and patients' quality of life. Although it is commonly seen, it lacks effective treatment. Our previous study found that ozone could alleviate neuropathic pain. Damage-associated molecular patterns (DAMPs) or Toll-like receptor 4 (TLR4) or tissue factor (TF)-mediated neuroinflammation and microcirculation disturbance is the main reason for CIPN. Suppressors of cytokine signaling (SOCS) 3 is an endogenous negative feedback regulator of inflammation via TLR4 inhibition. Materials and Methods: Oxaliplatin (L-OHP) was used to establish mice's CIPN model. Nociceptive responses were assessed by observing the ICR mice's incidence of foot regression in mechanical indentation response experiments. Cell signaling assays were performed by Western blotting and immunohistochemistry. The mouse leukemia cells of monocyte-macrophage line RAW 264.7 were cultured to investigate the effects of ozone administration on macrophage. Results: Ozone decreased the expression of TF in the blood and sciatic nerve. It upregulated the adenosine 5‘-monophosphate (AMP)-activated protein kinase (AMPK)-SOCS3 axis to relieve CIPN and inhibit TF expression in vivo. SOCS3 expression was induced by ozone to inhibit the p38/TF signaling in RAW 246.7 cells. Ozone also prevented L-OHP-induced sciatic nerve demyelination. Microglia activation was inhibited, and c-Fos and calcitonin gene-related peptide (CGRP) expression was decreased in the spinal dorsal horn via ozone. Conclusions: In this study, we demonstrated that ozone could alleviate CIPN by upregulating the AMPK-SOCS3 axis to inhibit TF expression, which is a potential treatment for CIPN. Keywords: AMPK, CIPN, L-OHP, ozone, SOCS3, TF, Author(s): Xiao-Tao Zhang [1,2]; Li-Juan Zong [3]; Ru-Meng Jia [3]; Xin-Miao Qin [3]; Shi-Rong Ruan [3]; Lin-Lin Lu [2,4]; Ping Wang [5]; Liang Hu (corresponding author) [3]; Wen-Tao Liu (corresponding [...]

The recent and brightest GRB 221009A observed by LHAASO marked the first detection of the onset of TeV afterglow, with a total of 7 GRBs exhibiting very high energy (VHE) afterglow radiation. However, consensus on VHE radiation of GRBs is still lacking. Multi-wavelength studies are currently a primary research method for investigating high-energy γ-ray astronomy. The limited sample of VHE GRBs, combined with their transient nature, hinders the progress of physical studies of GRBs. This paper aims to obtain useful information for GRB research through the properties of blazars, which share significant similarities with GRBs. By fitting high-quality and simultaneous multiwavelength spectral energy distributions with a one-zone leptonic model, the study explores the similarity of radiation properties of blazars and GRBs. A tight correlation between synchrotron and synchrotron self-Compton (SSC) emission luminosities suggests that blazars and GRBs share similar radiation mechanisms, to be specific, synchrotron radiation produces the observed X-ray photons, which also serve as targets for electrons in the SSC process. We hope that ground-based experiments can observe more GRBs in sub-TeV to confirm these findings.

Cuproptosis is characterized by the aggregation of lipoylated enzymes of the tricarboxylic acid cycle and subsequent loss of iron-sulfur cluster proteins as a unique copper-dependent form of regulated cell death. As dysregulation of copper homeostasis can induce cuproptosis, there is emerging interest in exploiting cuproptosis for cancer therapy. However, the molecular drivers of cancer cell evasion of cuproptosis were previously undefined. Here, we found that cuproptosis activates the Wnt/β-catenin pathway. Mechanistically, copper binds PDK1 and promotes its interaction with AKT, resulting in activation of the Wnt/β-catenin pathway and cancer stem cell (CSC) properties. Notably, aberrant activation of Wnt/β-catenin signaling conferred resistance of CSCs to cuproptosis. Further studies showed the β-catenin/TCF4 transcriptional complex directly binds the ATP7B promoter, inducing its expression. ATP7B effluxes copper ions, reducing intracellular copper and inhibiting cuproptosis. Knockdown of TCF4 or pharmacological Wnt/β-catenin blockade increased the sensitivity of CSCs to elesclomol-Cu-induced cuproptosis. These findings reveal a link between copper homeostasis regulated by the Wnt/β-catenin pathway and cuproptosis sensitivity, and suggest a precision medicine strategy for cancer treatment through selective cuproptosis induction.

Recently, memristors have garnered widespread attention as neuromorphic devices that can simulate synaptic behavior, holding promise for future commercial applications in neuromorphic computing. In this paper, we present a memristor with an Au/Bi3.2La0.8Ti3O12(BLTO)/ITO structure, demonstrating a switching ratio of nearly 103over a duration of 104 s. It successfully simulates a range of synaptic behaviors, including long-term potentiation and depression, paired-pulse facilitation, spike-timing-dependent plasticity, spike-rate-dependent plasticity etc. Interestingly, we also employ it to simulate pain threshold, sensitization, and desensitization behaviors of pain-perceptual nociceptor (PPN). Lastly, by introducing memristor differential pairs (1T1R-1T1R), we train a neural network, effectively simplifying the learning process, reducing training time, and achieving a handwriting digit recognition accuracy of up to 97.19 %. Overall, the proposed device holds immense potential in the field of neuromorphic computing, offering possibilities for the next generation of high-performance neuromorphic computing chips.

We present FossilMorph, a user-friendly and open access software that offers rapid measurement and statistical analysis based on fossil images. Routine steps of image processing technologies and algorithms designed for three types of organic-walled microfossils (i.e., spheroidal type, filamentous type, and broken type) have been introduced in order to illuminate the rationale of FossilMorph. Two case studies on the form genera Leiosphaeridiaand Siphonophycusare used to test the utility of FossilMorph on the study of micropalaeontology, especially the Proterozoic spheroidal acritarchs. Future development of FossilMorph may be extended to deal with complex morphology and community-type preservation for wider applications.

• L-PBFed Al- x Mg 2 Si alloy achieve the transition from crack-prone to crack-free. • Crack elimination is highly associated with eutectic filling and grain refinement. • The YS and EL of L-PBFed Al-9Mg 2 Si alloy are 397 MPa and 7.3 %, respectively. • Strengthening is mainly caused by the interaction between Mg 2 Si and the dislocation. Large solidification ranges and coarse columnar grains in the additively manufacturing of Al-Mg-Si alloys are normally involved in hot cracks during solidification. In this work, we develop novel crack-free Al-Mg 2 Si alloys fabricated by laser powder-bed fusion (L-PBF). The results indicate that the eutectic Mg 2 Si phase possesses a strong ability to reduce crack susceptibility. It can enhance the grain growth restriction factor in the initial stage of solidification and promote eutectic filling in the terminal stage of solidification. The crack-free L-PBFed Al- x Mg 2 Si alloys (x = 6 wt.%, 9 wt.%, and 12 wt.%) exhibit the combination of low crack susceptibility index (CSI), superior ability for liquid filling, and grain refinement. Particularly, the L-PBFed Al-9Mg 2 Si alloy shows improved mechanical properties (e.g. yield strength of 397 MPa and elongation of 7.3 %). However, the cracks are more likely to occur in the region near the columnar grain boundaries of the L-PBFed Al-3Mg 2 Si alloy with a large solidification range and low eutectic content for liquid filling. Correspondingly, the L-PBFed Al-3Mg 2 Si alloy shows poor bearing capacity of mechanical properties. The precise tuning of Mg 2 Si eutectic content can offer an innovative strategy for eliminating cracks in additively manufactured Al-Mg-Si alloy. [Display omitted] [ABSTRACT FROM AUTHOR]

Depression is a debilitating mental illness that severely threatens millions of individuals and public health. Because of the multifactorial etiologies, there is currently no cure for depression; thus, it is urgently imperative to find alternative antidepressants and strategies. Growing evidence underscores the prominent role of oxidative stress as key pathological hallmarks of depression, making oxidative stress a potential therapeutic target. In this study, we report a N-doped carbon dot nanozyme (CDzyme) with excellent antioxidant capacity for treating depression by remodeling redox homeostasis and gut microbiota. The CDzymes prepared via microwave-assisted fast polymerization of histidine and glucose exhibit superior biocompatibility. Benefiting from the unique structure, CDzymes can provide abundant electrons, hydrogen atoms, and protons for reducing reactions, as well as catalytic sites to mimic redox enzymes. These mechanisms collaborating endow CDzymes with broad-spectrum antioxidant capacity to scavenge reactive oxygen and nitrogen species (•OH, O2–•, H2O2, ONOO–), and oxygen/nitrogen centered free radicals. A depression animal model was established by chronic unpredictable mild stress (CUMS) to evaluate the therapeutic efficacy of CDzymes from the behavioral, physiological, and biochemical index and intestinal flora assessments. CDzymes can remarkably improve depression-like behaviors and key neurotransmitters produced in hippocampus tissues and restore the gut microbiota compositions and the amino acid metabolic functions, proving the potential in treating depression through the intestinal-brain axis system. This study will facilitate the development of intestinal flora dysbiosis nanomedicines and treatment strategies for depression and other oxidative stress related multifactorial diseases.

Metal–organic frameworks (MOFs) have attained broad research attention in the areas of sensors, resistive memories, and optoelectronic synapses on the merits of their intriguing physical and chemical properties. In this review, recent progress on the synthesis of MOFs and their electronic applications is introduced and discussed. Initially, the crystal structures and properties of MOFs encompassing optical, electrical, and chemical properties are discussed in brief. Subsequently, advanced synthesis methods for MOFs are introduced, categorized into hydrothermal approach, microwave synthesis, mechanochemical synthesis, and electrochemical deposition. After that, the various roles of MOFs in widespread applications, including sensing, information storage, optoelectronic synapses, machine learning, and artificial intelligence, are discussed, highlighting their versatility and the innovative solutions they provide to long-standing challenges. Finally, an outlook on remaining challenges and a future perspective for MOFs are proposed.

An eco-friendly superhydrophobic protective film (DTMS/TEOS silane film) was fabricated on sintered NdFeB substrate through the utilization of electrochemically assisted deposition technology. The structure, properties, and film-forming mechanism of dodecyltrime-thoxysilane (DTMS)/tetraethoxysilane (TEOS) silane films were comprehensively analyzed using Fourier transform infrared spectroscopy (FT-IR), scanning electron microscopy (SEM), X-ray photoelectron spectroscopy (XPS), potentiodynamic polarization curves and electrochemical impedance spectroscopy (EIS). Based on the test results, it can be determined that this film has a superhydrophobic property with a hydrophobicity angle of 152°. This special property can be attributed to the long alkyl chains in the DTMS molecule, the rough morphology, and the low surface energy of the DTMS/TEOS silane film. The surface of sintered NdFeB is coated with a layered three-dimensional network silane film that forms through the condensation of silanol substances. This film provides excellent corrosion resistance to the sintered NdFeB substrate, reducing its corrosion current density to 2.02×10−6A/cm2. Moreover, the impact of film on the magnetic characteristics of sintered NdFeB was assessed and found to be minimal.

• A double-peak age hardening is observed in the LPBF-processed Al-5Mg-2Si-3 Zn alloy. • β″ and η′ phases are sequentially precipitated in the peak aged states. • The hierarchical structures co-contribute to the high strength of alloy. Laser powder bed fusion (LPBF) with a high cooling rate delivers a huge potential in improving the solid solubility limit and subsequent precipitation strengthening. In this work, a double-peak age hardening was observed in the LPBF-processed Al-5Mg-2Si-3Zn alloy, in which the higher hardness value was 176 HV for the second peak and the lower hardness was 169 HV for the first peak. The yield strength was remarkably enhanced from the 349 MPa under as-built condition to the 434 MPa under as-aged condition. It was found that the double peak hardening was highly associated with Zn alloying, which promoted the formation of Zn segregation with overlapping of nanoscale eutectic Mg 2 Si cells in the as-built state. The aged microstructures were characterized by the uniformly distributed Zn solute and the precipitation of the secondary lath-shaped β″ phase, which is responsible for the first peak age strengthening. Further, the pre-existed β″ in conjunction with the newly formed needle-shaped (Mg, Zn)-rich Guinier-Preston (GP) zone and plate-shaped η′ precipitates co-contribute to the strengthening in the second peak. The synergistic strengthening of multiple precipitates is very effective and efficient for the development of additively manufactured aluminium alloys. [Display omitted] [ABSTRACT FROM AUTHOR]

The conversion of CO2into useful chemicals via photocatalysts is a promising strategy for resolving the environmental problems caused by the addition of CO2. Herein, a series of composite photocatalysts MOP@TpPa-CH3based on MOP-NH2and TpPa-CH3through covalent bridging have been prepared via a facile room-temperature evaporation method and employed for photocatalytic CO2reduction. The photocatalytic performances of MOP@TpPa-CH3are greater than those of TpPa-CH3and MOP-NH2, where the CO generation rate of MOP@TpPa-CH3under 10% CO2still reaches 119.25 μmol g–1h–1, which is 2.18 times higher than that under pure CO2(54.74 μmol g–1h–1). To investigate the structural factors affecting the photocatalytic activity, MOP@TBPa-CH3without C═O groups is synthesized, and the photoreduction performance is also evaluated. The controlling experimental results demonstrate that the excellent photoreduction CO2performance of MOP@TpPa-CH3in a 10% CO2atmosphere is due to the presence of C═O groups in TpPa-CH3. This work offers a new design and construction strategy for novel MOP@COF composites.

Bimetallic heterogeneous nanomaterials are widely used in photocatalysis to degrade toxic organic compounds. By design of the morphological characteristics of nanomaterials, their catalytic degradation properties can be better exerted. Here, the controllable preparation of Au and Pt bimetallic heterogeneous nanomaterials is achieved by a water phase synthesis method. By regulating the solution environment during growth processes carefully, the growth position of Pt on the Au nanobipyramids (Au NBPs) can be precisely controlled, and two different nanostructures are prepared, which are called Au@Pt nanobipyramids (Au NBP@Pt) and pyramid-like Au/Pt nanodumbbells (P-Au/Pt), corresponding to Pt nanoparticles on the surface and the tips of the Au NBPs. Based on Au NBP@Pt nanostructures, hollow Pt nanocages are obtained by etching Au. Then, Au is grown on the surface of Pt nanocages, and the Pt@Au hollow nanobipyramids (Pt@Au hollow NBPs) are prepared. We investigate the photocatalytic reduction rates of four nanostructures and find that Pt@Au hollow NBPs have the fastest catalytic reduction rate compared to other nanostructures. The reaction rate constant kof Pt@Au hollow NBPs is 7.84, 4.46, and 17.99 times that of P-Au/Pt, Au NBP@Pt, and R-Au/Pt, respectively. We carry out a comprehensive analysis of this result. We find that compared to the nonhollow structures, Pt@Au hollow NBPs have two strong plasmon resonances, which indicate electric resonance and magnetic resonance, resulting in strong light absorption and efficient electron transfer rate. In addition, Au@Pt hollow NBPs also have a large specific surface area. The convergence of the aforementioned reasons collectively contributes to the significantly increased catalytic rate. Our work provides a new approach for preparing hollow bimetallic heterogeneous nanomaterials, and the technology has great potential in photocatalysis applications and environmental governance.

With the integration of 5G NR (New Radio) into railway systems, the demand for enhanced positioning and trajectory tracking performance in High-Speed Train (HST) networks has grown. However, many existing train positioning schemes rely on traditional algorithms like the Extended Kalman Filter (EKF), which may fall short of meeting the precision requirements, particularly in 5G HST scenarios. Addressing this limitation, this paper draws on the concepts presented by Ko et al. (2022) and introduces an Improved Extended Kalman Filter (IEKF) using the Least Squares of Undermeasurement (LSU) technique, specifically tailored for nonlinear systems. The IEKF, expanding step by step, theoretically captures statistical properties of the Knorr set for any order prediction error, providing richer information on higher-order terms compared to the traditional EKF. Additionally, for a more intuitive comparison of the IEKF unfolded to different orders, a novel performance indicator is introduced. In conclusion, to validate the effectiveness of our proposed algorithm in real-world scenarios, we demonstrate its superior localization performance by comparing Mean Squared Error (MSE) and Mean Absolute Error (MAE) with traditional nonlinear localization algorithms. The comparisons are based on simulation examples involving train localization tracking and an industrial device ablation system.

Peroxymonosulfate (PMS) Fenton-like systems have emerged as promising alternatives to hydrogen peroxide (H2O2). Fenton systems are currently used in the industry owing to their highly efficient utilization rate of oxidizing agents and wide operating pH ranges. Heterogeneous Fenton-like catalysts are promising candidates in this regard. However, self-aggregation and generation of ambiguous reactive oxygen species greatly restrict their broad application in practical settings. Herein, a redox reaction between exfoliated MXene and KMnO4facilitates the in-situdeposition of MnO2nanoparticles on the surface of Ti-deficient vacancies of MXene (MXene/MnO2). Owing to the advantages of MXene with fast charge transfer and MnO2with strong PMS activation ability, the engineered MXene/MnO2@PVDF catalytic membrane exhibited enhanced activity and excellent long-term stability for various refractory organic pollutants. Experimental observations, combined with density functional theory calculations, revealed that the exposed Mn sites effectively promoted the generation of 1O2. Interestingly, the widespread pathway for the direct generation of 1O2viahigh-valent Mn-oxo phases has a high energy barrier (3.34 eV). In contrast, the pathway that uses the •OOH species as intermediates to produce 1O2is energetically more viable (1.84 eV). This work offers insights into the in-situengineering of transition metal-oxides on MXene-based membranes, facilitating their implementation in remediating micropollutant-contaminated environmental water.

In this paper, the certain relation between threshold voltage and flat‐band voltage to improve the image sticking for AMOLED display was investigated. Flat band state generally refers to a state in which the energy bands of each region in an ideal MOSFET system are flattened. However, it is requiring an additional voltage to flatten the energy band for actual MOS systems due to the difference in metal and semiconductor work function, which is called the flat‐band voltage. It is investigated that this flat‐band voltage is strongly linked to the short‐term image sticking.

•A deep learning-based framework is devised for the comprehensive molecular and prognostic stratifications of triple-negative breast cancer.•A tissue type classifier is developed and incorporated in the framework to decompose the tissue on whole slide images and replace manual annotation.•A novel online platform is built which can be clinically implemented and realize real-time one-stop prediction for new cases.

Sporadic or late-onset Alzheimer’s disease (LOAD) accounts for more than 95% of Alzheimer’s disease (AD) cases without any family history. Although genome-wide association studies have identified associated risk genes and loci for LOAD, numerous studies suggest that many adverse environmental factors, such as social isolation, are associated with an increased risk of dementia. However, the underlying mechanisms of social isolation in AD progression remain elusive. In the current study, we found that 7 days of social isolation could trigger pattern separation impairments and presynaptic abnormalities of the mossy fibre-CA3 circuit in AD mice. We also revealed that social isolation disrupted histone acetylation and resulted in the downregulation of 2 dentate gyrus (DG)-enriched miRNAs, which simultaneously target reticulon 3 (RTN3), an endoplasmic reticulum protein that aggregates in presynaptic regions to disturb the formation of functional mossy fibre boutons (MFBs) by recruiting multiple mitochondrial and vesicle-related proteins. Interestingly, the aggregation of RTN3 also recruits the PP2A B subunits to suppress PP2A activity and induce tau hyperphosphorylation, which, in turn, further elevates RTN3 and forms a vicious cycle. Finally, using an artificial intelligence-assisted molecular docking approach, we determined that senktide, a selective agonist of neurokinin3 receptors (NK3R), could reduce the binding of RTN3 with its partners. Moreover, application of senktide in vivoeffectively restored DG circuit disorders in socially isolated AD mice. Taken together, our findings not only demonstrate the epigenetic regulatory mechanism underlying mossy fibre synaptic disorders orchestrated by social isolation and tau pathology but also reveal a novel potential therapeutic strategy for AD.

ITER IF coils and the German WX-7 star simulator fusion reactor both use cyanate ester/epoxy resin as the matrix material for their insulation structure. The main reason is that cyanate ester/epoxy resin insulation materials have low-temperature mechanics and radiation resistance, and have extremely low rheological properties and long applicability periods. However, high-energy polymerization will occur when the two are mixed, releasing a huge amount of heat. This study used different molds to prepare cyanate ester/epoxy resin (mass ratio 4:6), and used DSC (Differential Calorimetry Scanning) thermal analyzer and temperature monitoring to study the curing behavior and thermal stability of cyanate ester-modified epoxy resin-based materials. After multiple optimizations, the resin was completely cured within a controllable range, and there was no significant change in the glass transition temperature before and after adjustment.

When the hydrostatic thrust bearings operate under conditions of high speed and heavy load, the oil film will be strongly sheared and squeezed, which will increase the temperature of the hydrostatic oil film, resulting in uneven deformation of the workbench and tribology in serious cases. The deformation of the friction pair greatly affects the stability of the workbench during operation, and then affects the machining accuracy. Taking the hydrostatic thrust bearings as the research object, the model of hydrostatic thrust bearings was established based on the fluid–thermosolid coupling theory, and the influencing factors of the deformation of the hydrostatic thrust bearings are analyzed using ANSYS Workbench software, and the influencing laws are discussed. Finally, the correctness of the simulation method is verified by experiments. The results show that the larger the lubricating oil viscosity, the greater the deformation of the guide surface and the oil pad. With the increase in the rotation rate, the deformation of the guide surface and the oil pad increases continuously. With the increase in the inlet flow rate, the deformation of the guide surface and the oil pad is continuously reduced. In engineering practice, on the premise of ensuring the bearings capacity, low-viscosity lubricating oil should be used as much as possible, the rotation rate should be lower, or the inlet flow rate should be increased.

An effective source reconstruction method (SRM) to predict the noise coupling from an unknown electromagnetic interference (EMI) source inside a shielding enclosure is proposed in this work. On account of the difficulty and time-consuming in phase measurement of near-field, the magnitude-only near-field scanning is employed to obtain the near-field above EMI sources in free space, and the leakage near-field from the slots and/or holes of a shielding enclosure after the EMI source is inserted into the enclosure. The infinitesimal dipole is used as the equivalent source of the EMI source. The proposed method can generally be divided into three steps. First, the positions and initial moments of the equivalent dipoles of an unknown EMI source without any shielding enclosure are derived by using dynamic differential evolution (DDE) algorithm. Second, the obtained dipoles are thereafter placed into an arbitrarily shaped shielding enclosure, and the full-wave simulation is implemented to build the relationship between the dipoles and the measured leakage field from the slots/holes of the shielding enclosure. Third, the dipole moments are thereby optimized by using the DDE method again based on the magnitude-only leakage field. It is worth highlighting that only a relatively small region of near-field on the leakage plane is sufficient for obtaining an effective equivalent dipole model in the shielding enclosure. The results of numerical simulation and physical measurement have verified the effectiveness of the proposed SRM for modeling unknown EMI sources in a shielding enclosure.

Polysaccharides in extracellular polymeric substances (EPS) can form a hybrid matrix network with proteins, impeding waste-activated sludge (WAS) fermentation. Amino sugars, such as N-acetyl-d-glucosamine (GlcNAc) polymers and sialic acid, are the non-negligible components in the EPS of aerobic granules or biofilm. However, the occurrence of amino sugars in WAS and their degradation remains unclear. Thus, amino sugars (∼6.0%) in WAS were revealed, and the genera of Lactococcusand Zoogloeawere identified for the first time. Chitin was used as the substrate to enrich a chitin-degrading consortium (CDC). The COD balances for methane production ranged from 83.3 and 95.1%. Chitin was gradually converted to oligosaccharides and GlcNAc after dosing with the extracellular enzyme. After doing enriched CDC in WAS, the final methane production markedly increased to 60.4 ± 0.6 mL, reflecting an increase of ∼62%. Four model substrates of amino sugars (GlcNAc and sialic acid) and polysaccharides (cellulose and dextran) could be used by CDC. Treponema(34.3%) was identified as the core bacterium via excreting chitinases (EC 3.2.1.14) and N-acetyl-glucosaminidases (EC 3.2.1.52), especially the genetic abundance of chitinases in CDC was 2.5 times higher than that of WAS. Thus, this study provides an elegant method for the utilization of amino sugar-enriched organics.

Cardiac rehabilitation, a comprehensive exercise-based lifestyle and medical management, is effective in decreasing morbidity and improving life quality in patients with coronary heart disease. Endothelial function, an irreplaceable indicator in coronary heart disease progression, is measured by various methods in traditional cardiac rehabilitation pathways, including medicinal treatment, aerobic training, and smoking cessation. Nevertheless, studies on the effect of some emerging cardiac rehabilitation programs on endothelial function are limited. This article briefly reviewed the endothelium-beneficial effects of different cardiac rehabilitation pathways, including exercise training, lifestyle modification and psychological intervention in patients with coronary heart disease, and related experimental models, and summarized both uncovered and potential cellular and molecular mechanisms of the beneficial roles of various cardiac rehabilitation pathways on endothelial function. In exercise training and some lifestyle interventions, the enhanced bioavailability of nitric oxide, increased circulating endothelial progenitor cells (EPCs), and decreased oxidative stress are major contributors to preventing endothelial dysfunction in coronary heart disease. Moreover, the preservation of endothelial-dependent hyperpolarizing factors and inflammatory suppression play roles. On the one hand, to develop more endothelium-protective rehabilitation methods in coronary heart disease, adequately designed and sized randomized multicenter clinical trials should be advanced using standardized cardiac rehabilitation programs and existing assessment methods. On the other hand, additional studies using suitable experimental models are warranted to elucidate the relationship between some new interventions and endothelial protection in both macro- and microvasculature.

The size complementarity between a host and a guest is a critical prerequisite for selective molecular recognition. Numerous artificial host systems have been designed to achieve size-selective recognition and chemical transformations. However, the inherent challenge lies in the fixed selectivity governed by a size-exclusion mechanism dictated by the host’s structure. Modulating the selectivity necessitates structural modifications of hosts. Examples of host systems with modifiable size-selectivity are rare. In this study, we present an approach to modulating guest-selectivity via endo-functionalization of supra-molecular cavities. Heteroleptic Pd2A3B-cages are constructed with a Pd2A3precursor or directly from palladium ions and relevant ligands. Furthermore, the simultaneous and selective formation of multiple hetero-cages was achieved by simply mixing PdIIand the free ligands in a one-pot reaction. The fidelity of the assembly process relies on cooperative steric control at the periphery and within the cavity of the hetero-cages, respectively. The central steric group also functions as the endohedral moiety. Variations of the endohedral groups facilitate facile and modular derivatization of the microenvironment within the cages, enabling the fine-tuning of guest binding affinities as evidenced by titration experiments. This strategy offers a new solution for the development of customized host structures.

This technical note studies the analysis and control problem of rectangular descriptor systems (RDSs). First, we present the state-equivalent form of RDSs using the left (right) inverse of a matrix and demonstrate its equivalence to the original system in terms of admissibility. Subsequently, we introduce a minimum-order compensator (MOC) and establish a series of rank criteria for its application. The MOC is utilized to ensure that the closed-loop system is generalized regular, impulse-free, and admissible. In addition, we provide a necessary and sufficient condition in the form of bilinear matrix inequalities for designing the MOC, along with an iterative algorithm for computing the gains. Finally, the effectiveness of our methods are demonstrated through two numerical examples and a circuit example.

In practice, the model structure, parameters and time-delay of the actual process may vary simultaneously. However, the general identification methods of the 3 items are performed with separate procedures which is very inconvenient in practical application. In view of the fact that variable selection procedure can ensure a compact model with robust input-output relation and in order to explore the feasibility of variable selection algorithm for the simultaneous identification of process structure, parameters and time-delay, non-negative garrote (NNG) algorithm is introduced and applied to system identification and the corresponding procedures are presented. The application of NNG variable selection algorithm to the identification of single input single output (SISO) system, multiple input multiple output (MIMO) system and Wood-Berry tower industry are investigated. The identification accuracy and the time-series variable selection results are analyzed and compared between NNG and ordinary least square (OLS) algorithms. The derived excellent results show that the proposed NNG-based modeling algorithm can be utilized for simultaneous identification of the model structure, parameters and time-delay with high precision.

In this letter, an efficient two-stage adaptive beamforming algorithm is proposed for sparse linear array synthesis. In the first stage, the unitary matrix pencil method is employed to synthesize a sparse linear array to generate a pattern with a low sidelobe level (SLL). Next, by serving the results obtained previously as a constraint, an adaptive beamforming method is implemented to enhance the anti-interference ability of the array while maintaining the shape of sparse patterns as much as possible. Moreover, an adjustable factor is introduced to enhance the capability of seeking feasible solutions, and new constraints are raised to broad nulls, respectively. Four numerical examples involving low SLL, single interferer, multiple interferers and broad nulls are provided to validate the flexibility and effectiveness of the proposed method. Simulation results show the superior anti-interference performance and high computational efficiency of the proposed algorithm.

In this article, we present a phaseless source reconstruction method based on dynamic differential evolution (DDE) combined with the least square and regularization for effectively constructing the equivalent dipoles to represent the real electromagnetic interference (EMI) sources, which enables the accurate reconstruction of near fields. The conventional DDE method is sensitive to the initial values of dipole moments, and the improper initialization may lead to the failure of its convergence. To avoid this, the proposed method employs least square (LSQ) to predict the dipole moment levels during initialization. This eliminates the need of multiple attempts to determine the appropriate range of dipole moments and thus saves the computation time. Moreover, LSQ is embedded in each iteration of DDE to further manipulate the dipole moments. The effectiveness and accuracy of the proposed method are verified through both simulation and experiment. This article offers a new and more reliable alternative for accurately reconstructing the near fields of EMI sources.

The total saponins from Aralia elata(Miq.) Seem fruits and rachises were isolated by microwave-assisted extraction (MAE), ultrasonic extraction, heat reflux extraction, and soxhlet extraction. These techniques were compared from four perspectives: extraction kinetics, saponin yield, disintegration to the raw materials, and antioxidant activity. MAE had the highest extraction efficiency, shortest time, and greatest disintegration to the raw materials. The extracts obtained with MAE had the highest antioxidant activities, as the DPPH free radical scavenging abilities of the fruit and rachis extracts were 70.86 ± 1.33 and 50.85 ± 1.68 mg VCg−1md, respectively. The MAE parameters were optimized by the response surface methodology. The optimum conditions (92% and 91% ethanol, solid-to-liquid ratios of 1:30 and 1:22, microwave powers of 530 W and 400 W, microwave times of 40 s and 54 s) provided extraction yields of 2.98 ± 0.037 mg g−1and 0.94 ± 0.026 mg g−1from the fruits and rachises, respectively.

Temperature-sensitive genic male sterility (TGMS) lines are widely used in the breeding of hybrid crops1,2, but by what means temperature as a general environmental factor reverses the fertility of different TGMS lines remains unknown. Here, we identified an ArabidopsisTGMS line named reversible male sterile(rvms) that is fertile at low temperature (17 °C) and encodes a GDSL lipase. Cytological observations and statistical analysis showed that low temperature slows pollen development. Further screening of restorers of rvms, as well as crossing with a slow-growth line at normal temperature (24 °C), demonstrate that slowing of development overcomes the defects of rvmsmicrospores and allows them to develop into functional pollen. Several other ArabidopsisTGMS lines were identified, and their fertility was also restored by slowing of development. Given that male reproductive development is conserved3, we propose that slowing of development is a general mechanism applicable to the sterility–fertility conversion of TGMS lines from different plant species.

This article investigates the performance optimization structure and algorithm of the electromagnetic levitation (EML) system. Firstly, a functionalized control structure is proposed by the incremental attachment of a controller gain system. Under this control structure, the predesigned controller is designed for nominal performance while the controller gain system handles performance optimization. This is of great help because the system trajectory of the highly dynamic and open-loop unstable EML system can be contained in the admissible region during the optimization of the controller gain system. Secondly, it is demonstrated that optimizing the incrementally attached controller gain system (under the proposed control structure) for performance optimization is equivalent to optimizing the predesigned controller. The advantage of the equivalence is that the predesigned control system needs not to be modified and the incremental attachment can be performed even when the EML system is running. Furthermore, ${Q}$ -learning algorithm is presented for the real-time implementation of the proposed structure and the incrementally attached controller gain system. Finally, the effectiveness of the structure and algorithm is validated on the EML system. Note to Practitioners—The EML system is key to maglev transportation. It is open-loop unstable, highly dynamic, and its accurate model is generally unavailable. On the other hand, the levitation performance may not be optimal given the predesigned controller. Moreover, during the long-term operation, it is desired in practice that the controller of EML system can be upgraded in a friendly fashion. This article presents a control structure that optimizes the levitation performance in an incremental style, such that the predesigned controller does not need to be modified. The incremental attachment can be realized via the onboard CAN network or UDP channel. ${Q}$ -learning algorithm optimizes the incrementally attached controller gain system without distabilizing the levitation stability of the EML system under the proposed control structure. In such a way, the open-loop and highly dynamic characteristics can be handled and the friendly implementation style can be achieved.

The microstructure of coke has an important influence on its thermal properties. The solution loss reactions of coke in CO2and H2O atmospheres were investigated by in situ observation. The results showed that the isotropic components had a more vigorous reaction than the anisotropic components, and the solution loss reaction of the fine-grained mosaic structure was faster than that of the coarse-grained mosaic structure under the CO2and H2O atmospheres. The coarse-grained mosaic structure and the flowing structure had a relatively higher anti-erosion ability in the CO2atmosphere than in the H2O atmosphere, and there was no distinct difference in the solution loss of the isotropic structure under the CO2and H2O atmospheres. The electron probe microanalysis showed that the Al–Si–Fe compounds in the carbon matrix had positive influence on the solution loss reaction of the anisotropic structure. The iron compounds were able to destroy the pore walls of coke and accelerate the solution loss rate of coke.

It is critically important for companies to screen new product projects before they are launched to the market. So far, many approaches have been developed for tackling the process of screening product innovations. Due to uncertain, vague and incomplete information as well as dynamically complex process regarding to new product development (NPD), a fuzzy linguistic approach employed linguistic assessments and the fuzzy-set-based computation is reasonable for screening new products. However, such a fuzzy linguistic approach faces with various defects and limitations, such as loss of information, failing in considering the aspects related to human nature on uncertain subjective judgments etc. These defects and limitations lead to a dilemma, i.e., it’s very difficult to screen new product projects reasonably and precisely. In this paper, we propose a notion of proportional 3-tuple to represent a linguistic assessment and related ignoring information, and a preference-preserving proportional 3-tuple transformation for the unification of linguistic assessments represented by proportional 3-tuples between two different linguistic term sets. On this basis, a proportional 3-tuple fuzzy linguistic representation model for screening new product projects is developed. It is shown that the proposed model is flexible to handle uncertain, vague and incomplete information related to screening new product projects. It not only allows evaluators to express their subjective judgments with different confidence levels, but is also able to deal with incomplete linguistic assessments. Ultimately, the proposed model also improves the precision and reasonability of the screening result.

In order to meet the strict requirements of the reliability and safety of the commercial operation of the maglev train, it is necessary to carry out the control performance evaluation of the suspension system. However, at present, variance is often used as the evaluation index of control system. This method cannot get accurate performance evaluation results when the system is affected by non-Gaussian disturbance. Therefore, aiming at the non-Gaussian control performance evaluation of maglev train suspension system, a control performance evaluation method of maglev train suspension system based on minimum entropy is proposed in this paper. In view of the non-Gaussian characteristics in the operation data of maglev train suspension system, this paper introduces the feedback invariant entropy of non-Gaussian system, and then realizes the evaluation and analysis of the control performance of maglev train suspension system based on the minimum entropy criterion. Finally, combined with the evaluation and analysis of the operation data of Changsha medium and low-speed maglev train, this paper verifies the effectiveness and accuracy of the proposed performance evaluation method for the control performance evaluation of maglev train suspension system.

The exoskeleton is employed to assist humans in various domains including military missions, rehabilitation, industrial operation, and activities of daily living (ADLs).Walking is a fundamental ADL, and exoskeletons are capable of reducing the activation and metabolism of lower extremity muscles through active assistance during walking. To improve the performance of active assistance strategy, this article proposes a framework using an active hip exoskeleton. Subsequently, it correlates to an already established Bayesian-based human gait recognition algorithm, with a particular focus on linear and circular walking within industrial and ADL contexts. In theorizing this strategy for exoskeletons, this study further reveals, in part, the activation characteristics of human hip muscles for the instruction and regulation of active assistance duration and onset timing. This proposed active assistance strategy provides new insights for enhancing the performance of assistive robots and facilitating human robot interaction within the context of ADLs.

Purpose: Osteoporosis has been a widespread concern for older women, especially postmenopausal women. Thyroid function is crucial for bone metabolism. However, the relationship between thyroid function variation within thyroxine reference range and bone mineral density (BMD) remains ambiguous. The objective of this study was to evaluate the effect of subclinical hypothyroidism or hyperthyroidism on total spinal BMD in postmenopausal women. Methods: Based on data from the National Health and Nutrition Examination Survey (NHANES) 2007–2010, multivariable weighted logistic regression was used to evaluate the relationships between total spine BMD and TSH among postmenopausal women aged ≥50. Results: After accounting for a number of variables, this study discovered that the middle TSH tertile was associated with a decreased probability of osteoporosis. Additionally, the subgroup analysis revealed that postmenopausal women over the age of 65 or people with an overweight BMI had a clearer relationship between total spine BMD and TSH. Conclusion: The total spinal BMD had a positive relationship with thyroid stimulating hormone in postmenopausal women, and that appropriate TSH level (1.38–2.32 mIU/L) was accompanied by higher total spinal BMD.

Fault diagnosis of railway assets has drawn the interest of both the scholarly and engineering communities. Federated learning (FL) enables training models across distributed assets to preserve data privacy and reduce high data transfer costs, which has been applied in fault diagnosis. However, the imperfect data transmission problem due to communication errors easily results in low accuracy of FL-based fault diagnosis in the railway system. To solve the problem, a sequential and asynchronous federated learning framework is proposed for fault diagnosis of railway point machines (RPMs) in this work. First, a dual-branch network is proposed as the global model in asynchronous FL for reducing parameters, while maintaining high accuracy. Second, a time cycle mechanism based on sequential Kalman filtering is proposed for reducing the negative impact of data communication errors. Finally, experimental results demonstrates that the proposed method enhances the applicability of online RPM fault diagnosis training in real deployment scenarios.

Due to the range-angle-dependent beampattern, frequency diverse array (FDA) has attracted extensive attention. However, the inherent time-varying and range-angle coupling problems of the FDA are still troublesome. In this article, a quasi-time-invariant focusing beamforming scheme for retrodirective (RD)-FDA is proposed to alleviate the above tough problems. Specifically, linear and semicircular RD-FDAs are explored to achieve automatic beam alignment of the desired target without phase weights. By establishing an artificial neural network (ANN) framework, frequency offsets can be optimized to match the radiation requirements. By minimizing the loss value of the network, the desired focusing pattern can be realized. Then, optimized frequency offsets are periodically inverted to limit the signal phase, which allows flexible control of the spatial variation range of the mainlobe beam. Simulation results show the effectiveness and superiority of the proposed scheme to achieve quasi-time-invariant beamforming.

Oxygen saturation (SpO2) is a vital parameter in clinical practice. Clinical research suggests that SpO2 may significantly decrease in patients with lung infections before evident symptoms, such as COVID-19, appear. Therefore, monitoring the cardiopulmonary status of individuals with lung disease is indispensable. Although contactless SpO2 measurements perform well in laboratory settings, their clinical application still faces several challenges. To bridge this gap, a camera-based contactless SpO2 measurement foundation model called CCSpO2Net is proposed, which is appropriate for both clinical and laboratory settings. This approach is based on a spatial feature extractor (SFE) and a global temporal feature extractor and estimator (GTFEE). The SFE employs two residual networks to enhance interchannel correlations and capture spatial information. The GTFEE module consists of a multilayer perceptron mixer (MLP-Mixer) network and an SpO2 estimator. The MLP-Mixer operation facilitates global temporal information exchange across positions by rearranging and swapping the features along the channel dimension. Additionally, the segment anything model (SAM) was first utilized to accurately extract skin regions at the pixel level to measure SpO2. Finally, we are the first to explore the topic of contactless SpO2 estimation based on facial videos in a clinical environment; remarkable results were achieved, with a mean absolute error (MAE) of 1.79 and a root-mean-square error (RMSE) of 2.85 on the intensive care unit (ICU) dataset. Moreover, the extensive experimental results further validate that CCSpO2Net outperforms the state-of-the-art approaches significantly, achieving MAEs of 0.54 (improved by 16.7%) and 2.48 (improved by 101.6%) on the PURE and smartphone camera oximetry (SCO) datasets, respectively.

In response to the pressing demand for accurately transforming reactive near field into 3-meter far field for unknown radiation sources in electromagnetic interference (EMI) testing, this article introduces an innovative hybrid method that integrates a deep convolutional neural network (DCNN) with the plane wave spectrum. By leveraging plane wave expansion, the spatial magnetic near-field data are converted into the spectrum domain, serving as the input for the DCNN model. DCNN’s output is the 3-meter electric far field. In comparison with the method that directly utilizes the spatial magnetic near field as the DCNN input, the proposed method notably improves the generalization of the DCNN model. It enables the output of DCNN (3-meter far field) insensitive to variations in the near-field scanning height. A randomly distributed dipole array is used to generate the pairs of spectral near field and 3-meter far field for training the DCNN. Moreover, the proposed method shows the exceptional robustness against noise interference, even for the signal-to-noise ratio (SNR) is as low as −5 dB. Both numerical and measurement examples demonstrate that the trained DCNN can accurately predict the 3-meter far field based on magnetic reactive near fields acquired at various scanning heights and from different radiation sources.

Kalman filter (KF) is highly valued in engineering for its simplicity, small storage, and real-time processing. However, KF is optimal for linear filters and not as effective for nonlinear ones. In this article, we propose a high-precision nonlinear filter, the deep neural network Kalman filter (DKF), which combines KF and a neural network model. DKF’s estimation process follows the Kalman filter approach. To maximize the use of model information, we establish DKF by merging the Kalman prediction and update outcomes as neural network input features and training the input–output nonlinear mapping model online. We also introduce a fusion filter, FDKF, based on KF and DKF. Simulation results demonstrate that, for linear Gaussian systems, DKF outperforms KF, and FDKF outperforms both DKF and KF in offline iterative prediction.

Accurate ambient perception via multi-object detection and tracking is instrumental for autonomous vehicles. This article addresses two main challenges when operating solely on 3-D light laser detection and ranging (LiDAR) point clouds: the classification of objects with similar geometric structures and tracking under the commonplace setting of low-frequency sensing. First, we design a semantically enhanced feature aggregation module that fuses features learned from two branches with different resolutions and depths. Subsequently, the extracted semantic information combined with our proposed Margin Loss allows the re-identification module to extract time-invariant geometric features. These features are fused with the positional information provided by the detector by a cluster-based Earth's mover distance algorithm along with conflation to improve the tracking stability. Extensive experiments on nuScenes demonstrate that our proposed model outperforms the state-of-the-art methods for both LiDAR-based 3-D object detection and tracking. In particular, we report an increase of 1.1% in average multi-object tracking accuracy, as well higher mean average precision for detection by 6.2% and 7.5% on motorcycle and bicycle, respectively.

The infrared imaging performance of type-II superlattice (T2SL) has been widely used in civil and aerospace fields. Due to the special band structure and material properties, the T2SL has the advantages of the adjustable bandgap, a wide range of spectral response (2– $30~\mu \text{m}$ ), effectively inhibiting Auger recombination, low cost, and good uniformity in large areas. InAs/GaSb T2SL is considered to be a promising material to advance the performance of photoelectric devices, especially in the long-wave range. With the development of science and technology and the continuous efforts of scientists, the T2SL has made great progress in material growth, device design, and process research. However, there is still a gap to the theoretically values. This article discusses the reasons for the performance degradation of T2SL devices, especially the surface and bulk dark current factors, and introduces various methods to reduce dark current and advantages and disadvantages of these methods.

Surrogate-assisted evolutionary algorithms (EAs) have been proposed in recent years to solve data-driven optimization problems. Most existing surrogate-assisted EAs are for centralized optimization and do not take into account the challenges brought by the distribution of data at the edge of networks in the era of the Internet of Things. To this end, we propose edge–cloud co-EAs (ECCoEAs) to solve distributed data-driven optimization problems, where data are collected by edge servers. Specifically, we first propose a distributed framework of ECCoEAs, which consists of a communication mechanism, edge model management, and cloud model management. This communication mechanism is to avoid deadlock during the collaboration of edge servers and the cloud server. In edge model management, the edge models are trained based on local historical data and data composed of new solutions generated by co-evolutionary and their real evaluation values. In cloud model management, the black-box prediction functions received from edge models are used to find promising solutions to guide the edge model management. Moreover, two ECCoEAs are implemented, which proves the generality of the framework. To verify the performance of algorithms for distributed data-driven optimization problems, we design a novel benchmark test suite. The performance on the benchmarks and practical distributed clustering problems shows the effectiveness of ECCoEAs.

In this work, a dual-polarized endfire phased antenna array with a low-profile is proposed for millimeter-wave (mmW) beam steering application. The proposed antenna array is constructed by a $1\times5$ subarray with vertical polarization (V-pol) realized by novel SIW horn antenna and a $1\times4$ subarray with horizontal polarization (H-pol) realized by quasi-Yagi antenna. Characteristic mode theory (CMT) is employed to guide the wideband design for both V-pol and H-pol elements in the dual-polarized framework. A wide impedance bandwidth for V-pol radiation is 24.6–29.8 GHz, while that for H-pol radiation is 23.37–35.47 GHz. By integrating both subarrays, an overlapping bandwidth from 24.5 to 30 GHz is realized within a total profile of $0.15\lambda _{0} $ ; in addition, high isolation of −20 dB is achieved among most operation band for each element. Furthermore, the maximum realized gain for V-pol radiation and H-pol radiation are 11.4 and 11.6 dBi, respectively. Meanwhile, a wide beam scanning range of −45° to 42° for V-pol radiation and −45° to 39° for H-pol radiation with realized gain over 7 dBi is realized. In virtue of low-profile, wide bandwidth, easy integration, high isolation, and high gain, the proposed antenna array provides a promising candidate for future mmW vehicular applications.

The antenna array synthesis problem has long been known as a tough issue, which attracts considerable interest to explore high-performance low-complexity optimization techniques. In this article, an efficient artificial neural network (ANN) for time-modulated arrays (TMAs) synthesis is proposed. By defining the equivalent excitation properly, TMA synthesis can be first transformed to a generalized phased array optimization. Next, a two-stage ANN framework composed of two encoders and a universal decoder is established to optimize the equivalent excitation, and then a single-input single-output (SISO) sinc−1-ANN is proposed to solve inverse of $\text {sinc}\left ({\cdot }\right)$ efficiently. To achieve fast and accurate pattern prediction, the decoder is pretrained to be a real-time array analyzer, while the encoder is designed as an array synthesizer to develop online training. By minimizing the loss function related to radiation pattern and equivalent excitation, the desired pattern can be achieved. Then, with the help of the pretrained SISO sinc−1-ANN, the static excitation coefficient, switch-on duration, and starting time are acquired. Simulation results of different types of desired TMA patterns are provided to verify the superiority, effectiveness, and efficiency of the proposed approach.

The yaw damper is of great significance to the dynamic performance of high-speed trains. In this work, the influence of different installation angles of yaw dampers on dynamic responses and wheel wear is investigated. Firstly, the detailed dynamic model of a high-speed train is developed, which includes all kinds of nonlinear factors, such as wheel/rail contact relationship and nonlinear characteristics of various damping, and the influence of the installation angle of the yaw damper on dynamic performances of railway vehicles is investigated. Then the wheel wear prediction procedure is also proposed to obtain the effect of the installation angle of the yaw dampers on wheel wear evolution. The results show that the nonlinear critical speed of railway vehicles gradually increases with the increasing lateral distance of the yaw damper at the frame side. While critical velocity gradually decreases with increasing vertical installation distance of the yaw damper at the car body side. The maximum wheel wear depth will also gradually increase with increasing vertical distance at the car body side and increasing lateral distance of the yaw damper at the frame side when the vehicle runs on the tangent track. Moreover, it is also noticed that the wear depth of the wheel on both sides will gradually increase with the increasing vertical installation distance of the yaw damper at the car body side when the vehicle runs on a curved track. When the lateral installation angle of the yaw damper is less than 0°, the wheel wear rate remains constant and the wear sensitivity of the wheel to lateral installation angle is low, if the lateral installation angle of the yaw damper is greater than 0°, wheel wear rate increases significantly with increasing lateral installation angle of the yaw damper.

With the gradual maturity of sequencing technology, many microbiome studies have published, driving the emergence and advance of related analysis tools. R language is the widely used platform for microbiome data analysis for powerful functions. However, tens of thousands of R packages and numerous similar analysis tools have brought major challenges for many researchers to explore microbiome data. How to choose suitable, efficient, convenient, and easy-to-learn tools from the numerous R packages has become a problem for many microbiome researchers. We have organized 324 common R packages for microbiome analysis and classified them according to application categories (diversity, difference, biomarker, correlation and network, functional prediction, and others), which could help researchers quickly find relevant R packages for microbiome analysis. Furthermore, we systematically sorted the integrated R packages (phyloseq, microbiome, MicrobiomeAnalystR, Animalcules, microeco, and amplicon) for microbiome analysis, and summarized the advantages and limitations, which will help researchers choose the appropriate tools. Finally, we thoroughly reviewed the R packages for microbiome analysis, summarized most of the common analysis content in the microbiome, and formed the most suitable pipeline for microbiome analysis. This paper is accompanied by hundreds of examples with 10,000 lines codes in GitHub, which can help beginners to learn, also help analysts compare and test different tools. This paper systematically sorts the application of R in microbiome, providing an important theoretical basis and practical reference for the development of better microbiome tools in the future. All the code is available at GitHubgithub.com/taowenmicro/EasyMicrobiomeR.Graphical Abstract

ABSTRACT Bamboo has been widely used as a rapidly renewable structural material to build permanent and temporary structures in past decades. However, the compressive bearing capacity of bamboo is relatively [...]

Recently, Ko et al. (2022) proposed a high-speed railway positioning scheme based on an improved Kalman filter using 5G NR signals. Although the proposal was promising, our research and analysis have revealed that the method has serious design flaws in the proposed filtering principles, rendering the algorithm infeasible. Specifically, the flaws are related to the computation and usability of high-order terms in the prediction error after Taylor expansion and prediction error derivation.

Suspension systems are significant for safe and comfort operation of high-speed trains. Health assessment is a useful tool to schedule maintenance plans of suspension systems, and furthermore ensure safety operation of high-speed railway transportation. In real operating condition, two problems, i.e. data imbalance and shortage of labelled data, result in difficult for health assessment of the suspension system using deep learning. In this work, a multi-sensor information fusion method, called as multi-sensor graph transfer network (MSGTN), is proposed in basis of deep transfer learning and graph neural network. In the proposed method, a domain-share multi-sensor graph neural network (MSGNN) is firstly proposed to extract features from vibration signals collected from three different positions in train vehicles. A graph-based fusion layer in MSGNN is proposed to fuse multi-sensor information by combining frequency response curves of the suspension system. The MSGTN mainly includes two parts in source and target domains respectively. In the source domain, a simple physical dynamic model of high-speed rail suspension system is built to generate labelled simulation datasets to pre-train MSGNN. In the target domain, the initial hyper-parameters of MSGNN are that of the pre-train model in the source domain. The labelled data in the target domain is fed to fine-tune MSGNN and then the final model for health assessment can be obtained by minimizing the loss function. The effectiveness of the proposed method was verified using real-work operation data.