Search

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.

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.