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In this article, a single-side-current feedback-based sliding-mode active damping (AD) control is proposed for high-speed permanent magnet synchronous machines with sine wave filter, which is effective for both the inverter-current feedback (ICF) and the motor-current feedback (MCF) only. A novel discrete-time sliding surface is developed to guarantee the stability of the sliding mode with a single-side current sensors, which is unnecessary to incorporate an additional state observer. Besides, a linear transformation is designed to obtain the dynamic of the reaching mode. Then, an adaptive reaching law is proposed and its stability is ensured by a discrete-time Lyapunov function. Experimental results demonstrate that the proposal has enhanced robustness against the parameter variation compared with existing methods.

As it is well recognized, LLCL filter effectively reduces the total inductance of the sinewave filter. In this article, a unified single-current feedback active damping method is proposed for LLCL-equipped high-speed permanent magnet synchronous motor. With the unified discrete model, this method can be utilized in both inverter current feedback-only and motor current feedback-only systems. The coefficient selection and simple digital implementation methods are developed to design the damping system efficiently. The proposed method performs well even with mismatched parameters, such as a $\pm$15% error in filter parameters and a $\pm$40% error in motor inductance. In addition, the study confirms the advantages of LLCL filter over LCL filter in terms of total inductance in high-speed motor drives. Finally, the effectiveness of the proposed method is validated by experimental results of a 12 kr/min(1000 Hz) HSPMSM.

Cytomegalovirus (CMV) can cause infection and critical diseases in hematopoietic stem cell transplantation (HSCT) recipients. This study aimed to explore the cumulative incidence and risk factors for CMV infection and disease among HSCT recipients in Taiwan.

Byline: Junjie. Ji, Yu. Yao, Fengju. Guan, Lijiang. Sun, Guiming. Zhang This meta-analysis investigated the efficacies of intra-arterial chemotherapy (IAC) plus intravesical chemotherapy (IVC) versus IVC alone in patients with [...]

Two-dimensional (2D) materials are promising candidates for spintronic applications. Maintaining their atomically smooth interfaces during integration of ferromagnetic (FM) electrodes is crucial since conventional metal deposition tends to induce defects at the interfaces. Meanwhile, the difficulties in picking up FM metals with strong adhesion and in achieving conductance match between FM electrodes and spin transport channels make it challenging to fabricate high-quality 2D spintronic devices using metal transfer techniques. Here, we report a solvent-free magnetic electrode transfer technique that employs a graphene layer to assist in the transfer of FM metals. It also serves as part of the FM electrode after transfer for optimizing spin injection, which enables the realization of spin valves with excellent performance based on various 2D materials. In addition to two-terminal devices, we demonstrate that the technique is applicable for four-terminal spin valves with nonlocal geometry. Our results provide a promising future of realizing 2D spintronic applications using the developed magnetic electrode transfer technique.

Excellent electrocatalytic CO2reduction reaction activity has been demonstrated by transition metals and nitrogen-codoped carbon (M–N–C) catalysts, especially for transition-metal porphyrin (MTPP)-based catalysts. In this work, we propose to use one-step low-temperature pyrolysis of the isostructural MTPP-based metal–organic frameworks (MOFs) and electrochemical in situ reduction strategies to obtain a series of hybrid catalysts of Co nanoparticles (Co NPs) and MTPP, named Co NPs/MTPP (M = Fe, Co, and Ni). The in situ introduction of Co NPs can efficiently enhance the electrocatalytic ability of MTPP (M = Fe, Co, and Ni) to convert CO2to CO, particularly for FeTPP. Co NPs/FeTPP endowed a high CO faradaic efficiency (FECOmax= 95.5%) in the H cell, and the FECO> 90.0% is in the broad potential range of −0.72 to −1.22 VRHE. In addition, the Co NPs/FeTPP achieved 145.4 mA cm–2at a lower potential of −0.70 VRHEwith an FECOof 94.7%, and the CO partial currents increased quickly to reach 202.2 mA cm–2at −0.80 VRHEwith an FECOof 91.6% in the flow cell. It is confirmed that Co NPs are necessary for hybrid catalysts to get superior electrocatalytic activity; Co NPs also can accelerate H2O dissociation and boost the proton supply capacity to hasten the proton-coupled electron-transfer process, effectively adjusting the adsorption strength of the reaction intermediates.

More demand-side flexible resources (DFRs) are participating in the frequency regulation of renewable power systems, whose heterogeneous characteristics have a significant impact on the system frequency response. Consequently, selecting suitable DFRs poses a formidable challenge for independent system operators (ISO). In this paper, a reserve allocation methodology for heterogeneous DFRs is proposed to manage the risk of power system frequency. Firstly, a performance curve is developed to describe the cost, capacity, and response speed of DFRs. Moreover, a clustering method for multiple distributed DFRs is conducted to calculate the aggregated performance curves and uncertainty coefficients. Then, the frequency security criterion considering DFRs’ performance is constructed, whose linearity makes it can be easily coupled into the system scheduling model and solved. Furthermore, a risk management model for DFRs considering frequency-chance-constraint is proposed to make a trade-off between cost and frequency security. Finally, the model is transformed into mixed integer second-order cone programming (MISOCP) and solved by the commercial solver. The proposed model is validated by the IEEE 30 and IEEE 118 bus systems.

Metal oxide ion-gated transistors (MOIGTs) have garnered significant attention within the sensing domain due to their potential for achieving heightened sensitivity while consuming minimal energy across diverse scenarios. By harnessing the advantageous combination of metal oxides’ high carrier mobility and facile surface customization, coupled with the potent signal amplification capabilities of ion-gated transistors, MOIGTs offer a promising avenue for discerning biomolecules, overseeing chemical reactions, pH levels, as well as facilitating gas or light determination. Over the past few decades, the MOIGT field has made remarkable strides in refining device physics, enhancing material properties, showcasing robust sensing capabilities, and broadening its application spectrum. These advancements have simultaneously unveiled new challenges and opportunities, necessitating interdisciplinary expertise to fully unlock the commercial potential of MOIGTs. In this comprehensive review, we offer a snapshot of this swiftly evolving technology, delve into its current applications, and provide insightful recommendations for future directions in the coming decade.

IMPORTANCE: The incidence of brain metastasis is increasing in patients with metastatic breast cancer. Treatments to extend the control of brain metastasis are urgently required. OBJECTIVE: To investigate whether the addition of an induction treatment of bevacizumab, etoposide, and cisplatin (BEEP) improves brain-specific progression-free survival (PFS) after whole-brain radiotherapy (WBRT). DESIGN, SETTING, AND PARTICIPANTS: This open-label, randomized, multicenter clinical trial assessed patients with brain metastases from breast cancer (BMBC) in Taiwan from September 9, 2014, to December 24, 2018, with survival follow-up until December 31, 2021. Key inclusion criteria included metastatic brain tumors not suitable for focal treatment, WBRT naivety, age 20 to 75 years, and at least 1 measurable brain metastatic lesion. The primary end point was brain-specific PFS, with an expected hazard ratio of 0.60, a 2-sided α ≤ .20, and power of 0.8. INTERVENTIONS: Eligible patients were randomly assigned at a ratio of 2:1 to the experimental arm, which involved 3 cycles of BEEP followed by WBRT, or the control arm, which involved WBRT alone. MAIN OUTCOMES AND MEASURES: The primary end point was the determination of brain-specific PFS by local investigators according to the Response Evaluation Criteria in Solid Tumors, version 1.1, the initiation of other brain-directed treatment after WBRT, or death. Other key end points included brain-specific objective response rate after 8 weeks of BEEP treatment or WBRT and 8-month brain-specific PFS rate, PFS, and overall survival. RESULTS: A total of 118 patients with BMBC were randomized, with the intention-to-treat cohort comprising 112 patients. The median age was 56 years (range, 34-71 years), and 61 patients (54.5%) had ERBB2 (formerly HER2 or HER2/neu)-positive disease. The median (range) brain-specific PFS was 8.1 (0.3-29.5) vs 6.5 (0.9-25.5) months in the experimental and control arms, respectively (hazard ratio, 0.71; 95% CI, 0.44-1.13; P = .15; significant at predefined α ≤ .20). The brain-specific objective response rate at 2 months was not significantly different (BEEP treatment vs WBRT, 41.9% vs 52.6%), but the 8-month brain-specific PFS rate was significantly higher in the experimental group (48.7% vs 26.3%; P = .03). Adverse events were generally manageable with prophylactic granulocyte colony-stimulating factor treatment. CONCLUSIONS AND RELEVANCE: The findings show that induction BEEP before WBRT may improve the control of BMBC compared with using upfront WBRT, which could address an unmet need for an effective systemic treatment for intractable brain and extracranial metastases from metastatic breast cancer. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT02185352

Sunvozertinib is an oral, irreversible, and selective tyrosine kinase inhibitor that has a favourable safety profile and encouraging antitumour activity, as shown in phase 1 studies of patients with heavily pretreated non-small cell lung cancer (NSCLC) with EGFRexon 20 insertion mutation (exon20ins). We aimed to assess the antitumour efficacy of sunvozertinib in patients with platinum-pretreated locally advanced or metastatic NSCLC with EGFRexon20ins.

Toxicological studies have indicated that exposure to chlorinated paraffins (CPs) may disrupt intracellular glucose and energy metabolism. However, limited information exists regarding the impact of human CP exposure on glucose homeostasis and its potential association with an increased risk of developing gestational diabetes mellitus (GDM). Here, we conducted a prospective study with a nested case–control design to evaluate the link between short- and medium-chain CP (SCCPs and MCCPs) exposures during pregnancy and the risk of GDM. Serum samples from 102 GDM-diagnosed pregnant women and 204 healthy controls were collected in Hangzhou, Eastern China. The median (interquartile range, IQR) concentration of SCCPs was 161 (127, 236) ng/mL in the GDM group compared to 127 (96.9, 176) ng/mL in the non-GDM group (p< 0.01). For MCCPs, the GDM group had a median concentration of 144 (117, 174) ng/mL, while the control group was 114 (78.1, 162) ng/mL (p< 0.01). Compared to the lowest quartile as the reference, the adjusted odds ratios (ORs) of GDM were 7.07 (95% CI: 2.87, 17.40) and 3.34 (95% CI: 1.48, 7.53) in the highest quartile of ∑SCCP and ∑MCCP levels, respectively, with MCCPs demonstrating an inverted U-shaped association with GDM. Weighted quantile sum regression evaluated the joint effects of all CPs on GDM and glucose homeostasis. Among all CP congeners, C13H23Cl5and C10H16Cl6were the crucial variables driving the positive association with the GDM risk. Our results demonstrated a significant positive association between CP concentration in maternal serum and GDM risk, and exposure to SCCPs and MCCPs may disturb maternal glucose homeostasis. These findings contribute to a better understanding of the health risks of CP exposure and the role of environmental contaminants in the pathogenesis of GDM.

Two-dimensional transition metal nitrides offer intriguing possibilities for achieving novel electronic and mechanical functionality owing to their distinctive and tunable bonding characteristics compared to other 2D materials. We demonstrate here the enabling effects of strong bonding on the morphology and functionality of 2D tungsten nitrides. The employed bottom-up synthesis experienced a unique substrate stabilization effect beyond van-der-Waals epitaxy that favored W5N6over lower metal nitrides. Comprehensive structural and electronic characterization reveals that monolayer W5N6can be synthesized at large scale and shows semimetallic behavior with an intriguing indirect band structure. Moreover, the material exhibits exceptional resilience against mechanical damage and chemical reactions. Leveraging these electronic properties and robustness, we demonstrate the application of W5N6as atomic-scale dry etch stops that allow the integration of high-performance 2D materials contacts. These findings highlight the potential of 2D transition metal nitrides for realizing advanced electronic devices and functional interfaces.

This paper investigates the problem of decentralized multi-robot cooperative localization. This problem involves collaboratively estimating the poses of a group of robots with respect to a common reference coordinate system using robot-to-robot relative measurements and intermittent absolute measurements in a distributed manner. To address this problem, we present a decentralized fusion method that enables batch updating to handle relative measurements from multiple robots simultaneously. This method can improve both the accuracy and computational efficiency of cooperative localization. To reduce communication costs and reliance on connectivity, we do not maintain the inter-robot state correlations. Instead, we adopt a covariance intersection (CI) technique to design an upper bound that replaces unknown joint correlations. We propose an optimization method to determine a tight upper bound for the correlations in the joint update. The consistency and convergence of our proposed algorithm is theoretically analyzed. Furthermore, we conduct Monte Carlo numerical simulations and real-world experiments to demonstrate that the proposed method outperforms existing approaches in terms of both accuracy and consistency.

Intelligent fault diagnosis has attracted much attention in industrial processes. The difficulty of collecting fault samples and high price of labeling data, has led to a relative scarcity of labeled data for deep learning tasks in the field. To address this gap, we propose a domain knowledge-guided contrastive learning framework based on complementary data views for fault diagnosis with limited data. Seven data views of either time- or frequency-domains are introduced and designed first. Then, the framework extracts task-specific features by 1) considering complementary information provided by multiple data views to each other, and 2) embedding a domain knowledge-involved space as the guide for the learning process. The results on two bearing datasets show the proposed framework can produce diagnosis accuracies of 96.60% and 94.24% when just 5% of samples have labels. This study determines two pairs of complementary data views that can boost the performance of the proposed framework.

This article proposes a dynamic decoupling active damping current control method for the current-source inverter (CSI)-fed high-speed permanent magnet synchronous machine (HSPMSM) drive with low carrier ratios. In this system, the natural resonance frequency is relatively high compared with the conventional CSI-fed system due to the high fundamental frequency and low inductance of the HSPMSM, which makes the traditional capacitor-voltage-feedback-based active damping fail. To achieve the desired resonance damping effects, the discrete-time-model-based multistate feedback active damping is proposed, which can configure the resonance pole arbitrarily. Then, to solve the serious cross coupling under low carrier ratios, a low-frequency-equivalent-transfer-function-based dynamic decoupling method is proposed for the parts where the traditional decoupling method cannot be applied. On the above basis, an outer-loop proportional–integral controller is adopted to adjust the magnitude and phase of the open-loop transfer function. In this way, the desired open-loop cutoff frequency with enough phase and gain margin can be achieved under the wide fundamental frequency changes of the HSPMSM. Finally, the effectiveness of the proposed method is verified by experiments.

Keyphrase generation (KG) is an essential problem in many natural language processing (NLP) tasks. Deep learning keyphrase generation methods often combine the copy and generating action-aware probabilities to model keyphrase accuracy, ignoring the copy and generating specificities for each keyword. In this work, we design a novel KG method that utilizes reinforcement learning (RL), namely KGAgent, where a relative reward based RL agent makes effective controllable manipulations on the decoupled action-aware policies to further improve the keyphrase generability. While RL is unstable and hard to train, several strategies, including momentum mechanism, distribution alignment and proximal policy optimization, are employed to stabilize the actor-critic network. Additionally, the proposed method reflects algorithm scalability that it can be plugged into any existing deep learning methods under different training paradigms, and consistently achieves higher accuracy than the state-of-the-art methods on the scientific article and social media datasets.

In this article, a new analytical model for predicting the magnetic field of the U-shaped interior permanent magnet motor (IPMM) is proposed. In the polar coordinates, rectangular permanent magnets are equivalent to the combination of consistent fan-shaped subdomains, and after that, the Laplace or Poisson equation of each solution domain can be obtained. Furthermore, based on the harmonic modeling (HM) technique, the saturation characteristics of the magnetic bridge are accurately considered, and then its magnetic field distribution (MFD) and electromagnetic performances (EPs) can be obtained. The approach proposed in this article includes but is not limited to U-shaped IPMMs, and it can also be applied to spoke-type or V-shaped IPMMs with a minor modification.

This article proposes a hybrid analytical model (HAM) for predicting ac copper losses in hairpin windings. The HAM combines the nonlinear semi-analytical model (SAM) and the 1-D ac copper loss computation model to solve the inaccurate boundary solutions of the 1-D ac copper loss computation model when magnetic saturation occurs in the stator teeth. The HAM is used to study the ac losses of the hairpin windings with four different conductor arrangements under different current excitation conditions, and the versatility of the HAM is verified. The HAM has high calculation accuracy, and the calculation speed is 66% faster than the finite element (FE) model. The primary contribution of this research is to present a quick and simple approach for accurately predicting ac copper losses in hairpin windings with arbitrary conductor arrangements.

Study Design Retrospective cohort studyObjective Given changes in bone density induced by degenerative disease, general measures of bone health (ie DEXA) are inadequate to evaluate bone density in surgical areas of interest. Regional differences in HU in the cervical spine may influence surgical strategies. The purposes of our study were to determine whether cervical Hounsfield units (HU) vary by level, examine their relationship with age, comorbidities, and alignment, and propose a technique to measure HU in the lateral masses.Methods Two hundred twenty-four patients with degenerative spine pathology with a cervical computed tomography were included (2015-2019). Measurements were performed in each vertebral body (C2-T1; mid-axial, anterior-axial, posterior-axial, mid-coronal, and mid-sagittal) and 2 regions of the lateral masses (C3-C6; mid-cor, mid-sag). To evaluate reliability, 6 observers each measured 355 HU values, inter-relater reliability assessed with intraclass correlation coefficients Correlations of HU with age, BMI, comorbidities, and cervical alignment were evaluated.Results Bone density differed by level, with the lowest HU scores in the lower cervical spine (C6-T1) (P< .001). No correlations were found between LM HU and age, BMI, CCI, or alignment (P> .05). Increased kyphosis was weakly correlated with VB HU, while age and CCI showed moderate correlations with VB HU at all levels (P<.001). ICC for HU measurements were good to excellent for the VBs, but poor to moderate for the LMs.Conclusion Bone is least dense in the lower cervical spine. HU scoring is not reliable in the lateral masses. We recommend that a level-specific approach to bone density is considered in surgical planning.

Automated anesthesia promises to enable more personalized and precise anesthetic administration and free anesthesiologists from repetitive tasks, allowing them to focus on the most critical aspects of surgical care for patients. Current research has typically focused on creating simulated environments from which agents can learn. These approaches have demonstrated good experimental results, but are still far from clinical application. In this paper, Policy Constraint Q-Learning (PCQL), a data-driven reinforcement learning algorithm for solving the problem of learning strategies on real world anesthesia data, is proposed. Conservative Q-Learning is first introduced to alleviate the problem of Q function overestimation in an offline context. A policy constraint term is then added to agent training to keep the action distribution of the agent and the anesthesiologist consistent, ensuring the agent makes safer decisions in anesthesia scenarios. The effectiveness of PCQL was validated by extensive experiments on a real clinical anesthesia dataset we collected. Experimental results show that PCQL is predicted to achieve higher gains than the baseline approaches while maintaining good agreement with the reference dose given by the anesthesiologist, using less total dose, and being more responsive to the patient's vital signs. In addition, the confidence intervals of the agent were investigated and were founded to cover most of the clinical decisions of the anesthesiologist. Finally, an interpretable method, SHAP, was used to analyze the contributing components of the model predictions, increasing the transparency of the model.

Radical prostatectomy (prostate removal) is a standard treatment for clinically localized prostate cancer and is often followed by postoperative radiotherapy. Postoperative radiotherapy requires accurate delineation of the clinical target volume (CTV) and lymph node drainage area (LNA) on computed tomography (CT) images. However, the CTV contour cannot be determined by the simple prostate expansion after resection of the prostate in the CT image. Constrained by this factor, the manual delineation process in postoperative radiotherapy is more time-consuming and challenging than in radical radiotherapy. In addition, CTV and LNA have no boundaries that can be distinguished by pixel values in CT images, and existing automatic segmentation models cannot get satisfactory results. Radiation oncologists generally determine CTV and LNA profiles according to clinical consensus and guidelines regarding surrounding organs at risk (OARs). In this work, we design a cascade segmentation block to explicitly establish correlations between CTV, LNA, and OARs, leveraging OARs features to guide CTV and LNA segmentation. Furthermore, inspired by the success of the self-attention mechanism and self-supervised learning, we adopt SwinTransformer as our backbone and propose a pure SwinTransformer-based segmentation network with self-supervised learning strategies. We performed extensive quantitative and qualitative evaluations of the proposed method. Compared to other competitive segmentation models, our model shows higher dice scores with minor standard deviations, and the detailed visualization results are more consistent with the ground truth. We believe this work can provide a feasible solution to this problem, making the postoperative radiotherapy process more efficient.

In this work, four new examples of isostructural three-dimensional (3D) heterometallic organic frameworks (HMOFs) [PbM0.5(L)(H2O)]n(M = Mn (1), Co (2), Zn (3), Cd (4)) have been successfully synthesized based on the 2-tetrazolium-terephthalic acid (H3L) ligand. Due to the abundance of Lewis acid–base sites in the structure, they exhibit high catalytic activity for the cyanosilylation and Knoevenagel condensation reactions. The second ions (M) act as the main microenvironment for the Pb active sites to effectively regulate the catalytic activity of [PbM0.5(L)(H2O)]n. The experimental results showed that the effect of second metal ions Mn, Co, Zn, and Cd on the catalytic activity of [PbM0.5(L)(H2O)]ndecreases sequentially. Among them, 1was generally applicable to different substrates, the structure remained complete after five cycles and the conversion rate still reached about 97%. Combined with the catalytic mechanism, X-ray photoelectron spectroscopy (XPS) and kinetic analysis showed that the difference of electron cloud density around Pb(ii) and uncoordinated nitrogen of the structure is the main reason for the different catalytic activity. Among the four example catalysts, the structure of 1has the lowest binding energy for Pb 4f and the highest binding for N 1s. The difference in binding energy brings about a variation in the electron cloud density, thereby affecting the activity of the catalytic reaction. This study is expected to bring implications for the synthesis of HMOFs and the effect of second metal ions on catalytic performance.

In this article, a nonlinear semianalytical model (SAM) is presented to predict the magnetic field distribution (MFD) and electromagnetic performances (EPs) in the cubic spoke-type permanent magnet (PM) machine. To model the rectangular PMs, the rectangular PM is simplified as a combination of fan-shaped regions with different arc angles. Then, the MFD and EPs of the cubic spoke-type machines can be obtained by the harmonic modeling technique. Particularly, the saturation of the magnetic bridges is considered by the nonlinear iterative algorithm. The proposed nonlinear SAM is studied on a 12-slot/8-pole cubic PM prototype, and the nonlinear finite element model and experiment verify its correctness. The main contribution of this article is to present a general analytical modeling method for cubic spoke-type PM machines and consider the magnetic saturation of magnetic bridges.

Consortium contracting is a contracting model that China encourages and advocates. Due to the interest drive, members within the consortium are very prone to negative cooperation and midway withdrawal, which hinders the healthy development of the consortium. Therefore, this paper constructs a game model of EPC consortium cooperation evolution, analyzes the influence of different reward and punishment mechanisms on the cooperation of consortium members, and applies system dynamics to simulation. The results show that under the static reward and punishment and dynamic reward mechanism, the consortium cooperation is not stable; while under the dynamic punishment mechanism and the dynamic reward and punishment mechanism in which the maximum punishment is greater than the maximum reward, the evolution of consortium cooperation is gradually stable and the behavioral strategies are gradually unified. It also puts forward suggestions for measures conducive to stabilizing cooperation, which provide certain reference value for the internal management of consortium members' cooperation.

In response to the critical need for advanced solutions in medical imaging segmentation, particularly for real-time applications in diagnostics and treatment planning, this study introduces SM-UNet. This novel deep learning architecture efficiently addresses the challenge of real-time, accurate medical image segmentation by integrating convolutional neural network (CNN) with multilayer perceptron (MLP). The architecture uniquely combines an initial convolutional encoder for detailed feature extraction, MLP module for capturing long-range dependencies, and a decoder that merges global features with high-resolution CNN map. Further optimization is achieved through a tokenization approach, significantly reducing computational demands. Its superior performance is confirmed by evaluations on standard datasets, showing interaction times drastically lower than comparable networks—between 1/6 to 1/10, and 1/25 compared to SOTA models. These advancements underscore SM-UNet's potential as a groundbreaking tool for facilitating real-time, precise medical diagnostics and treatment strategies.

•The SUPER score has a good prognostic AUC (0.985), sensitivity (1.00) and specificity (0.92) for early detecting severe COVID-19 cases with optimal threshold 4.•The accuracy of SUPER score is equivalent to NEWS2 which was recommended by WHO to predict the severity and predict prognosis of COVID-19 patients.•Compared with COVID-19 patients with SUPER score<4, patients in high- risk group tended to have lower lymphocyte counts, IL-2, IL-4, and higher levels of fibrinogen, CRP, AST, and LDH.

Prussian blue analogues (PBAs) used as sodium ion battery (SIB) cathodes are usually the focus of attention due to their three-dimensional open frame and high theoretical capacity. Nonetheless, the disadvantages of a low working voltage and inferior structural stability of PBAs prevent their further applications. Herein, we propose constructing the Kx(MnFeCoNiCu)[Fe(CN)6] (HE-K-PBA) cathode by high-entropy and potassium incorporation strategy to simultaneously realize high working voltage and cycling stability. The reaction mechanism of metal cations in HE-K-PBA are revealed by synchrotron radiation X-ray absorption spectroscopy (XAS), ex situX-ray photoelectron spectroscopy (XPS), and in situRaman spectra. We also investigate the entropy stabilization mechanism via finite element simulation, demonstrating that HE-K-PBA with small von Mises stress and weak structure strain can significantly mitigate the structural distortion. Benefit from the stable structure and everlasting K+(de)intercalation, the HE-K-PBA delivers high output voltage (3.46 V), good reversible capacity (120.5 mAh g–1at 0.01 A g–1), and capacity retention of 90.4% after 1700 cycles at 1.0 A g–1. Moreover, the assembled full cell and all-solid-state batteries with a stable median voltage of 3.29 V over 3000 cycles further demonstrate the application prospects of the HE-K-PBA cathode.

Cognitive radio (CR), an important part of nextgeneration wireless communications networks, has been extensively studied to improve the spectrum utilization. The spectrum sensing and access functions are especially crucial for ensuring the secure and efficient spectrum sharing needs of CR networks. In this article, secondary user access control (SUAC) based spectrum access technique is investigated to improve spectrum efficiency and network safety, by which a jamming signal is carefully designed to break down unauthorized secondary users (UA-SUs) sensing result and keep reliable sensing result from authorized secondary users (A-SUs). First, SUAC techniques are described, and a jamming cancellation method based on oblique projection approach is proposed to guarantee the sensing result of authorized secondary users. Second, a prioritized SUAC approach is examined to ensure A-SUs with distinct spectrum access levels. Third, quadratic programming in massive multiple-input multiple-output based SUAC is presented to effectively design a jamming signal that combines minimizing jammer’s influences on A-SUs and maximizing influences on UA-SUs. Finally, some open research areas and challenges in SUAC are discussed.

Polycrystalline RAlGe (R = La, Ce, Pr) is prepared by arc melting and vacuum solid state reaction technologies. The crystal structures of RAlGe are refined with Rietveld method based on two models of the site-disorderly centrosymmetric I41/amdand the site-orderly noncentrosymmetric I41md. The structural model is suggested to follow the site-orderly noncentrosymmetric I41md, which agrees with the prediction of nontrivial topology. This structure consists of the special alternating stack of A/B layers formed by isosceles triangle Δ in the A layer and reversal isosceles triangle ∇ in the B layer along the aaxis, and the lack of space-inversion symmetry with the noncentrosymmetric structure may result in the formation of Weyl points on Fermi faces of RAlGe semimetals. Magnetization measurement confirms an antiferromagnetic transition temperature of 2.9 K for CeAlGe and a melting model is built for checking the consistency of experimental and predicted transition temperatures. The effective moment of polycrystalline CeAlGe is 2.49 μB, which is smaller than that of Ce3+due to the existence of the Ce4+state in CeAlGe. Polycrystalline PrAlGe exhibits an antiferromagnetic transition at 14.4 K, and its effective moment (3.89 μB) is more than that of Pr3+; the extra moment might be due to the exchanging interaction between itinerant electron in s conducting band and 4f electrons of Pr, i.e., f–sexchanging interaction. The thermal properties and valence electron structures of RAlGe have been studied with empirical electron theory (EET) of solids and molecules. It shows that cohesive energy and melting point of RAlGe are strongly related to their valence electron structures.

Oxidation and removal of highly toxic sulfides and amines are particularly important for environmental and human security but remain challenging. Here, incorporating an excellent photosensitizer, donor–acceptor–donor (D–A–D)-type 4,4′-(benzo[c][1,2,5]thiadiazole-4,7-diyl)dibenzoic (H2L), into metal–organic frameworks (MOFs) has been manifested to promote the charge separation, affording four three-dimensional (3D) MOFs (isostructural 1-Co/1-Znwith Co2/Zn2units, and 2-Gd/2-Tbwith Gd/Tb-cluster chains) as photocatalysts in the visible light-driven air–O2-mediated catalytic oxidation and removal of hazardous phenylsulfides and benzylamines. Impressively, structure–property correlation illustrated that the transition metal centers assembled in MOFs play an important role in the photocatalytic activity, and we can conclude that 1-Zncan be a robust heterogeneous catalyst possessing good light adsorption and fast charge separation in oxidation removal reactions of both benzylamines and phenylsulfides under visible light irradiation and room temperature with excellent activity/selectivity, stability, and reusability.

Separating acetylene (C2H2) from other light hydrocarbons and carbon dioxide (CO2) mixtures under mild conditions poses significant challenges due to the remarkably similar properties between C2H2and those gases. For the goal of C2H2separation, a F-functionalized organic linker, H2F-PyIP = 2-fluorine-5-(4-pyridyl)isophthalic acid, was designed, and the corresponding metal–organic framework (MOF), {[Co2(F-PyIP)2DMF]·4H2O}n(1), was constructed. The MOF with open channels decorated by the active sites of the F groups revealed the exceptional C2H2uptake and selectivity over CO2, C2H4, and CH4. The breakthrough experiments with different molar ratios of C2H2–C2H4, C2H2–CO2, and other gas mixtures further verified superior separation capacity of the MOF. In particular, the dynamic separation time intervals for gas mixtures (C2H2/CO2= 1:1, 1:5, 1:10, and 1:20) fell in the range 30–44 min, highlighting the potential of the MOF for tackling the challenging C2H2/CO2separation process.

Accurate segmentation of the hepatic vein can improve the precision of liver disease diagnosis and treatment. Since the hepatic venous system is a small target and sparsely distributed, with various and diverse morphology, data labeling is difficult. Therefore, automatic hepatic vein segmentation is extremely challenging. We propose a lightweight contextual and morphological awareness network and design a novel morphology aware module based on attention mechanism and a 3D reconstruction module. The morphology aware module can obtain the slice similarity awareness mapping, which can enhance the continuous area of the hepatic veins in two adjacent slices through attention weighting. The 3D reconstruction module connects the 2D encoder and the 3D decoder to obtain the learning ability of 3D context with a very small amount of parameters. Compared with other SOTA methods, using the proposed method demonstrates an enhancement in the dice coefficient with few parameters on the two datasets. A small number of parameters can reduce hardware requirements and potentially have stronger generalization, which is an advantage in clinical deployment.

Honeypots have proven to be an effective defense method for industrial control systems (ICSs). However, as attacker skills become more sophisticated, it becomes increasingly difficult to develop honeypots that can effectively recognize and respond to such attacks. In this article, we propose a neural network-based ICS honeypot scheme named NeuPot that improves security from two aspects: 1) honeypot interaction; and 2) cyber threats detection capability. NeuPot can respond to attacker requests depending on a specific industrial scenario without constant communication with the ICS and detect malicious traffic. To create this honeypot scheme, a new seq2seq time-series forecast model guided by Huber loss is designed to simulate the long-term changes in actual ICS physical processes. Second, a Modbus honeypot framework is created to react to changes in these ICS physical processes in their interactions with attackers and to capture various cyber threats against the ICS. Further, a novel loss function for industrial protocol-level malicious traffic detection is devised to identify known and unknown threats. According to our experiments, the proposed honeypot scheme is highly effective and outperforms state-of-the-art schemes in terms of interactivity and in detecting cyber threats.

Two-dimensional transition-metal dichalcogenides (2D TMDCs) are considered promising materials for optoelectronics due to their unique optical and electric properties. However, their potential has been limited by the occurrence of atomic vacancies during synthesis. While post-treatment processes have demonstrated the passivation of such vacancies, they increase process complexity and affect the TMDC’s quality. We here introduce the concept of pretreatment as a facile and powerful route to solve the problem of vacancies in MoS2. Low-temperature nitridation of the sapphire substrate prior to growth provides a nondestructive method to MoS2modification without introducing new processing steps or increasing the thermal budget. Spectroscopic characterization and atomic-resolution microscopy reveal the incorporation of nitrogen from the sapphire surface layer into chalcogen vacancies. The resulting MoS2with nitrogen-saturated defects shows a decrease in midgap states and more intrinsic doping as confirmed by ab initio calculations and optoelectronic measurements. The demonstrated pretreatment method opens up new routes toward future, high-performance 2D electronics, as evidenced by a 3-fold reduction in contact resistance and a 10-fold improved performance of 2D photodetectors.

While the global COVID-19 pandemic has subsided, microbial aerosol detection has become of high concern. Timely, accurate, and highly sensitive monitoring of microbial aerosols in indoor air is the basis for effective prevention and control of infectious diseases. At present, no commercial equipment or reliable technology can simultaneously control the detection time and limit at 6 h and 102CFU/mL, respectively. Based on the “safety size range” of particulate matter in the air, we propose a new method of microbial dilation detection, which enables the pathogen to grow rapidly and dramatically into a polymeric microsphere, larger in size than the coexisting aerosol particles. “Like a crane standing among chickens”, the microorganism can be easily visualized and counted. Different from routine chemical and biological sensing technologies, this method can achieve absolute counting of microbial particles, and the simple principles can be developed into devices for different life scenarios.

Peak alignment is a crucial step in liquid chromatography-mass spectrometry (LC-MS)-based large-scale untargeted metabolomics workflows, as it enables the integration of metabolite peaks across multiple samples, which is essential for accurate data interpretation. Slight differences or fluctuations in chromatographic separation conditions, however, can cause the chromatographic retention time (RT) shift between consecutive analyses, ultimately affecting the accuracy of peak alignment between samples. Here, we introduce a novel RT shift correction method based on the retention index (RI) and apply it to peak alignment. We synthesized a series of N-acyl glycine (C2–C23) homologues via the amidation reaction between glycine with normal saturated fatty acids (C2–C23) as calibrants able to respond proficiently in both mass spectrometric positive- and negative-ion modes. Using these calibrants, we established an N-acyl glycine RI system. This RI system is capable of covering a broad chromatographic space and addressing chromatographic RT shift caused by variations in flow rate, gradient elution, instrument systems, and LC separation columns. Moreover, based on the RI system, we developed a peak shift correction model to enhance peak alignment accuracy. Applying the model resulted in a significant improvement in the accuracy of peak alignment from 15.5 to 80.9% across long-term data spanning a period of 157 days. To facilitate practical application, we developed a Python-based program, which is freely available at https://github.com/WHU-Fenglab/RI-based-CPSC.

Deep learning algorithms have led to a series of breakthroughs in computer vision, acoustical signal processing, and others. However, they have only been popularized recently due to the groundbreaking techniques developed for training deep architectures. Understanding the training techniques is important if we want to further improve them. Through extensive experimentation, Erhan et al. (2010) empirically illustrated that unsupervised pretraining has an effect of regularization for deep learning algorithms. However, theoretical justifications for the observation remain elusive. In this article, we provide theoretical supports by analyzing how unsupervised pretraining regularizes deep learning algorithms. Specifically, we interpret deep learning algorithms as the traditional Tikhonov-regularized batch learning algorithms that simultaneously learn predictors in the input feature spaces and the parameters of the neural networks to produce the Tikhonov matrices. We prove that unsupervised pretraining helps in learning meaningful Tikhonov matrices, which will make the deep learning algorithms uniformly stable and the learned predictor will generalize fast w.r.t. the sample size. Unsupervised pretraining, therefore, can be interpreted as to have the function of regularization.

Vehicle-to-vehicle (V2V) communication applications face significant challenges to security and privacy since all types of possible breaches are common in connected and autonomous vehicles (CAVs) networks. As an inheritance from conventional wireless services, potential eavesdropping is one of the main threats to V2V communications. In our work, the anti-eavesdropping scheme in CAVs networks is developed through the use of cognitive risk control (CRC)-based vehicular joint radar-communication (JRC) system. In particular, the supplement of off-board measurements acquired using V2V links to the perceptual information has presented the potential to enhance the traffic target positioning precision. Then, transmission power control is performed utilizing reinforcement learning, the result of which is determined by a task switcher. Based on the threat evaluation, a multiple armed bandit problem is designed to implement the secret key switching procedure when it is needed. Through constant perception-execution loops (PELs), the security and confidentiality is improved for the authorized vehicles in their behavioral interactions with the illegal eavesdropper. Numerical experiments have presented that the developed approach has anticipated performance in terms of some risk assessment indicators.

Organic dyes as non-noble metal photosensitizers have attracted increasing attention due to their environmental friendliness and sustainability but suffer from fast deactivation and low stability. Here, we reported a fruitful strategy by the confinement and stabilization of visible light-active signal unit organic dyes within the metal–organic frameworks (MOFs) and developed a series of heterogeneous photocatalysts dye@UiO-66s [dye = fluorescein (FL)/rhodamine B (RhB)/eosin Y (EY), UiO-66s = UiO-66, and Bim-UiO-66]. It has been demonstrated that the encapsulated dyes can effectively sensitize MOF hosts and dominate the band structures and photocatalytic activities of dye@UiO-66s regardless of the ligand functionalization of MOFs. Photocatalytic experiments showed that these dye@UiO-66s exhibit enhanced activities relative to free dyes and among them, FL@Bim-UiO-66 displays excellent efficiencies toward the green synthesis of new carbon-bridged annulations, [1,2,5]thiadiazole[3,4-g]benzoimidazoles in the yield of up to 98% at room temperature with outstanding stability and reusability. Furthermore, the intramolecular cyclization intermediate was captured and characterized by the single-crystal X-ray diffraction analysis.

Several randomized controlled trials have reported the effects of vegetarian diets on blood lipids and glucose homeostasis in adults, but not in overweight or obese individuals. Thus, the purpose of this study was to evaluate the effects of vegetarian diets on blood lipids and glucose homeostasis in overweight or obese adults by systematic review and meta-analysis. We searched Medline, Embase, and the Cochrane Library through October 2021. We chose to include overweight or obese patients in the studies of the vegetarian diet for metabolic control. Seven trials with a total of 783 overweight or obese adult were included in the meta-analysis. The analysis of the data revealed that the vegetarian diets significantly reduced low-density lipoprotein cholesterol (LDL-C) (WMD, −0.31; 95% CI, −0.46 to −0.16), total cholesterol (TC) (WMD, −0.37; 95% CI, −0.52 to −0.22), and HbA1c (WMD (%), −0.33; 95% CI, −0.55 to −0.11). The vegetarian diets had an elevated effect on blood triglycerides (WMD, 0.29; 95% CI, 0.11–0.47). However, there were no significant effects of vegetarian diets on high-density lipoprotein cholesterol (HDLC), Fasting plasma glucose(FPG), and HOMA-IR in these individuals. The results of this study suggest that vegetarian diets effectively reduce LDL-C, TC, and HbA1c levels, thus functioning as a promising therapeutic strategy for improving the metabolic dysfunction in overweight or obese individuals. However, further large-scale clinical trials are required to confirm the validity of these findings.

A main challenge for radar-aided millimeter wave (mmWave) vehicle-to-infrastructure (V2I) communication application, is that it requires mitigating the mutual interference between vehicular radar and communication operating at same frequency bands. This paper considers the joint design of the multiple-input multiple-output (MIMO) transmit waveform and receive filter bank for road side unit (RSU)-mounted radar in a spectrally crowded V2I communication environment. With the criterion of maximizing the average signal-to-interference-plus-noise ratio (SINR), a non-convex problem, which involves the weighted-sum waveform energy over the overlayed space-frequency bands, energy and similarity constraints, is formulated. An iterative algorithm is proposed to solve the joint optimization problem. At each iteration, the transmit waveform is optimized based on the alternating direction method of multipliers (ADMM) method with a significantly lower computational complexity. As a consequence, accurate location information derived from the optimized radar is used to reduce the beam training overhead of V2I communication links. Finally, simulation results display the effectiveness of the devised method in finding feasible and enhanced solutions, importantly outperforming several counterparts.

Weak magnetic flux leakage detection is a recently emerged and widely used stress detection technique for ferromagnetic materials. It has been used to analyze the stress in relation to the ferromagnetic material because of the change in the measurement signal near the inspected device caused by the stress. Weak magnetic flux leakage detection of ferromagnetic materials under normal stress [stress perpendicular to the stress concentration zone (SCZ)] is studied for the contact problem. The weak magnetic flux leakage detection signal of compressive stress under inclined conditions has not been available for quantitative analysis. In this article, a quantitative model of magnetic charge based on the linearity of the magnetic-stress angle is developed to determine the effect of the magnetic-stress angle on the magnetic signal. Some basic phenomena in the experiments can be verified by the theory of the quantitative model of linear magnetic charge based on the magnetic-stress angle proposed in this article. In particular, the magnetic-stress angle under inclined conditions provides quantitative results on the effect of stress signal angle on the magnetic signal.

Gut microbiota–host co-metabolites serve as essential mediators of communication between the host and gut microbiota. They provide nutrient sources for host cells and regulate gut microenvironment, which are associated with a variety of diseases. Analysis of gut microbiota–host co-metabolites is of great significance to explore the host–gut microbiota interaction. In this study, we integrated chemical derivatization, liquid chromatography–mass spectrometry, and molecular networking (MN) to establish a novel CD-MN strategy for the analysis of carboxylated metabolites in gut microbial–host co-metabolism. Using this strategy, 261 carboxylated metabolites from mouse feces were detected, which grouped to various classes including fatty acids, bile acids, N-acyl amino acids, benzoheterocyclic acids, aromatic acids, and other unknown small-scale molecular clusters in MN. Based on the interpretation of the bile acid cluster, a novel type of phenylacetylated conjugates of host bile acids was identified, which were mediated by gut microbiota and exhibited a strong binding ability to Farnesoid X receptor and Takeda G protein-coupled receptor 5. Our proposed strategy offers a promising platform for uncovering carboxylated metabolites in gut microbial–host co-metabolism.

This article proposes a pole placement-based current control method for the current-source inverter (CSI) fed permanent magnet synchronous machine (PMSM) drive with eliminating capacitor voltage sampling. First, a generalized $N$-order feedback form of motor current and CSI output current is proposed in the stationary $\alpha \beta$ frame, for the arbitrary pole placement of the control plant. With the proposed pole placement method, the resonance frequency is moved closer to the Nyquist frequency and the resonance peak is damped. On this basis, a linear controller is proposed in the synchronous $dq$ frame for the high-performance current control of PMSM. Moreover, the control parameter selection method is presented based on the consideration of open-loop crossover frequency and phase margin (PM). In this way, the desired PM and enough gain margin can be achieved, thus guaranteeing the stability and robustness of the system. Finally, the effectiveness of the proposed method is verified by lab-scaled experiments.

Herein, we used the 4-fluoro-[1,1′-biphenyl]-3,4′,5-tricarboxylic acid (H3fbptc) ligand to design and construct a new metal–organic framework (MOF), [Cu3(fbptc)2(H2O)3]·3NMP (1), which possesses rich accessible metal sites and F functional groups in the porous walls and shows high uptake for C2H2(119.3 cm3g–1) and significant adsorption selectivity for C2H2over CH4(14.4) and CO2(3.6) at 298 K and 100 kPa. In particular, for the gas mixtures of C2H2–CH4and C2H2–CO2, the MOF reveals large breakthrough time ratios (C2H2/CH4= 13, C2H2/CO2= 5.9), which are particularly prominent in dynamic breakthrough experiments, also confirming the excellent potential for the practical separation of C2H2from two-component mixtures (C2H2–CH4and C2H2–CO2) and even three-component mixtures (C2H2–CO2–CH4).

The notorious low-temperature sensitivity of contemporary lithium-ion batteries (LIBs) has long restricted their low-temperature applications. Introduction of high-content low-freezing-point solvents significantly expands the low-temperature operation border of LIBs. However, they react violently with metallic lithium (Li) plated at low temperatures (−20°C or lower), resulting in severe gas generation and a limited lifespan. We reveal the mechanism of gas generation and develop a high-concentration ethyl acetate (EA)-based electrolyte. The dense and uniform solid electrolyte interphase formed by the joint decomposition of rich anions and additive effectively passivate the plated Li at low temperatures. The proposed electrolyte enables the LiNi0.8Co0.1Mn0.1O2(NCM811)/graphite pouch cell to be stably cycled at −20°C and 0.2 C for a record-breaking lifetime of more than 1 year (1,400 cycles). The NCM811/graphite pouch cell exhibits good fast-charging capability (0.5 C) at −20°C with a capacity decay rate as low as 0.0097% per cycle during 4,000 cycles.

Exploring emerging materials with enhanced optical nonlinearities at low power levels with ultrafast response and small footprints is of great interest for information processing, communication, sensing, and quantum systems. Recent progress on nonlinear metamaterials and metasurfaces suggests promising solutions to overcome the limitations of nonlinear materials in nature. Here we present a design concept for highly enhanced saturable absorption effect based on subwavelength-thick (<1/5λ0) hybrid graphene-plasmonic metasurface structures in infrared wavelengths. Our theoretical and experimental results demonstrated that, by exciting nonequilibrium carriers inside nanoscale hotspots, one could not only enhance the saturable absorption in graphene, but also reduce the saturation fluence by over 3 orders of magnitude (from ∼1 mJ/cm2to ∼100 nJ/cm2). Our pump–probe measurement results suggested an ultrashort saturable absorption recovery time (<60 fs), which is ultimately determined by the relaxation dynamics of photoexcited carriers in graphene. We also observed pulse narrowing effects in our devices based on the autocorrelation measurement results. Such design concepts can be tailored via structure engineering to operate in broader wavelength ranges up to mid- and far- infrared spectral regions. These ultrafast low-saturation fluence saturable absorber designs can enable low-threshold, compact, self-starting mode-locked lasers, laser pulse shaping, and high-speed optical information processing.

Study Design: Comparative cohort study.Objective: Factors that influence the lower instrumented vertebra (LIV) selection in adult cervical deformity (ACD) are less reported, and outcomes in the cervicothoracic junction (CTJ) and proximal thoracic (PT) spine are unclear.Methods: A prospective ACD database was analyzed using the following inclusion criteria: LIV between C7 and T5, upper instrumented vertebra at C2, and at least a 1-year follow-up. Patients were divided into CTJ (LIV C7-T2) and PT groups (LIV T3-T5) based on LIV levels. Demographics, operative details, radiographic parameters, and the health-related quality of life (HRQOL) scores were compared.Results: Forty-six patients were included (mean age, 62 years), with 22 and 24 patients in the CTJ and PT groups, respectively. Demographics and surgical parameters were comparable between the groups. The PT group had a significantly higher preoperative C2-C7 sagittal vertical axis (cSVA) (46.9 mm vs 32.6 mm, P= 0.002) and T1 slope minus cervical lordosis (45.9° vs 36.0°, P= 0.042) than the CTJ group and was more likely treated with pedicle-subtraction osteotomy (33.3% vs 0%, P= 0.004). The PT group had a larger correction of cSVA (−7.7 vs 0.7 mm, P= 0.037) and reciprocal change of increased T4-T12 kyphosis (8.6° vs 0.0°, P= 0.001). Complications and reoperations were comparable. The HRQOL scores were not different preoperatively and at 1-year follow-up.Conclusions: The selection of PT LIV in cervical deformities was more common in patients with larger baseline deformities, who were more likely to undergo pedicle-subtraction osteotomy. Despite this, the complications and HRQOL outcomes were comparable at 1-year follow-up.