The impact inertial piezoelectric actuators (IIPAs) are promising to be miniaturized as micro actuators due to their simple structure and high positioning resolution. In this work, a novel method is proposed to reduce the dependence on the inertial mass by exerting auxiliary friction on the inertial mass. To verify the validity of the method, a traditional IIPA (TIIPA) and an auxiliary friction IIPA (AFIIPA) are designed. The static analysis is conducted to determine their structural parameters. Then, their size and weight are identified as 9.6 mm×10 mm×7 mm and 0.8 g (the lightest IIPA to our knowledge), respectively. The dynamic analysis and experiments are performed, and the results demonstrate that under the same experimental conditions, the AFIIPA has lower sensitivity to inertial mass reduction and better output performances than those of the TIIPA. The maximum speed, output force, and resolution of the AFIIPA are improved by 81.8 %, 67.2 %, and 30.9% respectively, compared to the TIIPA. Besides, the AFIIPA has balanced comprehensive performances with extremely light weight compared with some previous micro actuators. This study provides a new miniaturization method for the IIPAs, which could advance the further development and applications of the micro IIPAs.
Traditional cancer therapy methods, especially those directed against specific intracellular targets or signaling pathways, are not powerful enough to overcome tumor heterogeneity and therapeutic resistance. Oncolytic peptides that can induce membrane lysis-mediated cancer cell death and subsequent anticancer immune responses , has provided a new paradigm for cancer therapy. However, the clinical application of oncolytic peptides is always limited by some factors such as unsatisfactory bio-distribution, poor stability, and off-target toxicity. To overcome these limitations, oncolytic polymers stand out as prospective therapeutic materials owing to their high stability, chemical versatility, and scalable production capacity, which has the potential to drive a revolution in cancer treatment. This review provides an overview of the mechanism and structure-activity relationship of oncolytic peptides. Then the oncolytic peptides-mediated combination therapy and the nano-delivery strategies for oncolytic peptides are summarized. Emphatically, the current research progress of oncolytic polymers has been highlighted. Lastly, the challenges and prospects in the development of oncolytic polymers are discussed.
Optical switch is a promising technology for high bandwidth communication in data centers. In this paper, we demonstrate a 3×3 switch with p-i-n phase shifters. With pull-push configuration, the average on-chip insertion losses at 1550 nm of 3×3 switch are 3.38 ± 0.7 dB and 4.04 ± 1.42 dB for “all-cross” and “all-bar” states, respectively. The optical crosstalk is lower than -10.57 dB from 1530 to 1565 nm for the worst case. The measured rise/fall time of the switch upon electrical tuning is 2.4(8.0) ns.
In this paper, the concept of fusion topology is proposed to tackle the development trend and control issue of hybrid parallel multi-converter applications and to achieve better performance in transient and steady-state operations. Compared to previously fixed frequency compensation strategies, data-driven finite-state machine (FSM) control can determine which voltage source converter (VSC) dominates the operation or whether two VSCs operate simultaneously rather than giving the specific target operation to each VSC alone. The results are verified by a real-time digital simulator (RTDS) with the hardware-in-the-loop (HIL) experiments. The 20kVA RTDS experimental results show that the transient time is shorter by 53.8%− 85.7%, and power loss is smaller by 28.6%− 78.3%, respectively, compared with the previous methods.
Due to manufacturing process and yield constraints, scaling GPU performance via increasing chip area becomes difficult. In the meanwhile, the demand for high computational throughput is increasing for high performance computing applications. As an alternative, multi-chip module GPU (MCM-GPU) achieves performance scalability via integrating multiple GPU chip modules (GPMs) on the same package. However, large MCM-GPU systems are susceptible to voltage noise effects, which cause voltage instability during program execution and result in energy inefficiency. In this work, we first model and analyze the voltage noise of MCM-GPUs at architecture level in detail. We characterize the voltage noise distributions of MCM-GPUs at different levels and under various design parameters. We further propose two architecture level voltage noise mitigation approaches, including GPM aware mitigation (GAM) and droop magnitude aware smoothing (DMAS), that leverage the voltage noise characteristics of MCM-GPUs. Evaluation shows both techniques are effective in reducing the voltage droop magnitudes and achieve good energy savings with negligible performance degradation.
Distributed acoustic sensing (DAS) has been moderately utilized in seismic exploration as a consequence of its advantages including low-cost, high-resolution, high-sensitivity, and strong resistance to high temperatures. Nonetheless, signals in seismic data received by the DAS system are often seriously contaminated by a massive amount of background noise and some missing traces also decrease the quality of DAS seismic data. Following the great success in numerous fields of data processing, convolutional neural networks (CNNs) have also been applied to seismic data denoising as well as reconstruction and achieved more remarkable performance compared with traditional methods. However, relatively few CNN-based studies have been done on the simultaneous denoising and reconstruction of seismic data, especially for the complex DAS seismic data with a low signal-to-noise ratio. In this study, we propose a multi-cascade network structure in combination with a channel attention mechanism (MCA-Net) to achieve the simultaneous denoising and reconstruction of DAS seismic data by just using a unified trained model. Particularly, the proposed MCA-Net consists of three cascades that can extract abundant features from DAS seismic data with different resolutions. Furthermore, we deploy some channel attention modules and aggressive blocks to MCA-Net to fuse these aforementioned multi-resolution features and thus improving the feature extraction ability of the whole network. Meanwhile, a high-quality training dataset composed of paired clean complete and noisy incomplete patches is used to train MCA-Net in a supervised fashion. Some synthetic and real DAS seismic data are utilized to demonstrate the effectiveness of MCA-Net and our method exhibits better denoising and reconstruction performance than some traditional methods and two existing CNN architectures.
Molecular dynamics (MD) is one of the most crucial computer simulation methods for understanding real-world processes at the atomic level. Reactive potentials based on the bond order concept have the ability to model dynamic bond breaking and formation with close to quantum mechanical (QM) precision without actually requiring expensive QM calculations. In this paper, we focus on the adaptive intermolecular reactive empirical bond-order (AIREBO) potential in LAMMPS for the simulation of carbon and hydrocarbon systems on the new Sunway supercomputer. To achieve scalable performance, we propose a parallel two-level building scheme and periodic buffering strategy for the tailored data design to explore data locality and data reuse. Furthermore, we design two optimized nearest-neighbor access algorithms: the redistribution of accumulated coefficients algorithm and the double-end search connectivity algorithm. Finally, we implement parallel force computation with an AoS data layout and hardware/software co-cache. In addition, we have designed a low-overhead atomic operation-based load balancing method and vectorization. The overall performance of AIREBO achieves a speedup of nearly 20× on a single core group (CG), and more than 5× and 4× over an Intel Xeon E5 2680 v3 core and an Intel Xeon Gold 6138 core, respectively. Compared with the Intel accelerator package in LAMMPS, our performance further achieves 3.0× of an Intel Xeon E5 2680 v3 core and is better than that of an Intel Xeon Gold 6138 core. We complete the validation of the results in no more than 20.5 hours on a single node with 2,000,000 running steps (i.e., 1ns). Our experiments show that the simulation of 2,139,095,040 atoms on 798,720 ((1MPE+64CPEs) × 12,288 processes) cores exhibits a parallel efficiency of 88% under weak scaling.
Supramolecular polymers have attracted increasing attention in recent years due to their perfect combination of supramolecular chemistry and traditional polymer chemistry. The design and synthesis of macrocycles have driven the rapid development of supramolecular chemistry and polymer science. Pillar[n]arenes, a new generation of macrocyclic compounds possessing unique pillar‐shaped structures, nano‐sized cavities, multi‐functionalized groups, and excellent host–guest complexation abilities, are promising candidates to construct supramolecular polymer materials with enhanced properties and functionalities. This review summarizes recent progress in the design and synthesis of pillararene‐based supramolecular polymers (PSPs) and illustrates their diverse applications as adsorption and separation materials. All performances are evaluated and analyzed in terms of efficiency, selectivity, and recyclability. Typically, PSPs can be categorized into three typical types according to their topologies, including linear, cross‐linked, and hybrid structures. The advances made in the area of functional supramolecular polymeric adsorbents formed by novel pillararene derivatives are also described in detail. Finally, the remaining challenges and future perspectives of PSPs for separation‐based materials science are discussed. We envision that this review will inspire new researchers in different fields and stimulate creative designs of supramolecular polymeric materials based on pillararenes and other macrocycles for effective adsorption and separation of a variety of targets. This article is protected by copyright. All rights reserved
Solid polymer electrolytes (SPEs) encounter the challenge of balancing high ionic conductivity and mechanical strength. Ionic liquids, which are among the contenders to be used in high-performance supercapacitors, have difficulty infiltrating commercial polyolefin separators for combined applications. In this study, a novel SPE involving uniform infiltration in the micropores of commercial polyolefin separators with polyethylene oxide (PEO), lithium salt, and different proportions of added ionic liquid was developed. The composite membranes combining ionic liquid-filled SPE with polypropylene (PP) microporous separators simultaneously achieve excellent mechanical strength and high-ionic conductivity. The low wettability of pure ionic liquids and commercial polyolefin-based separators is addressed. The 70 wt% IL-filled solid electrolyte composite membrane (PLI(70)@PP) exhibits a high ionic conductivity (2.9 × 10⁻³ S cm⁻¹), low resistance at the electrolyte–electrode interface and excellent mechanical strength (128 MPa) at 25 °C. The all-solid-state supercapacitor using PLI(70)@PP exhibits a specific capacitance of 158 F g⁻¹ at 0.1 A g⁻¹ and stable cycle performance. The proposed method can be performed via high-volume roll-to-roll processing to obtain high-performance all-solid-state supercapacitors (ASSCs) for engineering applications.
Recently, contrastive clustering has demonstrated high performance in the field of deep clustering due to its powerful feature extraction capabilities. However, existing contrastive clustering methods suffer from inter-class conflicts and often produce suboptimal clustering outcomes due to the disregard of latent class information. To address this issue, we propose a novel method called Contrastive learning using Hierarchical data similarities for Deep Clustering (CHDC), consisting of three modules, namely the inter-class separation enhancer, the intra-class compactness enhancer, and the clustering module. Specifically, to induct the latent class information by utilizing the sample pairs with data similarities, the inter-class separation enhancer and the intra-class compactness enhancer handle negative and positive sample pairs, respectively, with distinct hierarchical similarities. Additionally, the clustering module aims to ensure the alignment of cluster assignments between samples and their neighboring samples. By designing these three modules that work collaboratively, inter-class conflicts are alleviated, allowing CHDC to learn more discriminative features. Lastly, we design a novel update method for positive sample pairs to reduce the likelihood of introducing erroneous information. To evaluate the performance of CHDC, we conduct extensive experiments on five widely adopted image classification datasets. The experimental results demonstrate the superiority of CHDC compared to state-of-the-art methods. Moreover, ablation studies demonstrate the effectiveness of the proposed modules.
This study aimed to explore the latent profiles of peer attachment and parent-child relationships and longitudinal transition patterns for within-person and within-sample profiles among a cohort of high school students. A cohort of 453 participants from China completed the above-mentioned measures with a six-month interval from grade 10 to 11. This study included 64.0% girls and 36.0% boys. Latent profile analysis (LPA) was used to explore the distinct profiles reflecting different response patterns for peer attachment and parent-child relationships at each time point. Latent transition analysis (LTA) was adopted to examine the membership of distinct latent profiles and how individuals move between profiles over time. Three latent profiles were identified with poor, moderate and good peer attachment and parent-child relationships. The stability of the same profile ranged from 0.601 to 0.803. The lowest transition probability was found between the poor and good profiles, whereas a higher transition probability from the poor and good profiles to the moderate profile was demonstrated. Patterns of peer attachment and parent-child relationships were associated with depression, anxiety, and Internet addiction (IA). This study advanced the knowledge of the heterogeneity and variation in peer attachment and parent-child relationships by a person-centered approach in a longitudinal study. The findings of the study direct selective interventions to populations with poor attachment and parent-peer relationships, and transitions from good to poor attachment direction across latent profiles of attachment and parent-peer relationships. Establishing secure attachment with peers and having good parent-child relationships may contribute to good adolescents’ mental health and protect them from excessive Internet use.
Our study aimed to investigate the prognostic value of neutrophil count to albumin ratio (NAR) in predicting short-term mortality of patients with decompensated cirrhosis (DC). A total of 623 DC patients were recruited from a retrospective observational cohort study. They were admitted to our hospital from January 2014 to December 2015. NAR of each patient was calculated and analyzed for the association with 90-day liver transplantation-free (LT-free) outcome. The performance of NAR and the integrated model were tested by a receiver-operator curve (ROC) and C-index. The 90-day LT-free mortality of patients with DC was 10.6%. NAR was significantly higher in 90-day non-survivors than in survivors (The median: 1.73 vs 0.76, P < 0.001). A threshold of 1.40 of NAR differentiated patients with a high risk of death (27.45%) from those with a low risk (5.11%). By multivariate analysis, high NAR was independently associated with poor short-term prognosis (high group: 5.07 (2.78, 9.22)). NAR alone had an area under the ROC curve of 0.794 and C-index of 0.7789 (0.7287, 0.8291) in predicting 90-day mortality. The integrated MELD–NAR (iMELD) model had a higher area under the ROC (0.872) and C-index (0.8558 (0.8122, 0.8994)) than the original MELD in predicting 90-day mortality. NAR can be used as an independent predictor of poor outcomes for patients with DC during short-term follow-up.
An electrochemical sensing approach for ultrasensitive DNA methyltransferase (MTase) activity assay is proposed. After specific cleavage reaction in the presence of a methylated state, strand displacement polymerization (SDP) is initiated in the solution. The product of upstream SDP further triggers downstream SDP, which enriches abundant electrochemical species at the electrode. The whole process is quite convenient with shared enzymes. Due to the cascade signal amplification, ultrahigh sensitivity is promised. Inhibitor screening results are also demonstrated to be good. Besides, target MTase can be accurately determined in human serum samples, confirming excellent practical utility. This work provides a reliable approach for the analysis of MTase activity, which is of vital importance for related biological studies and clinical applications.
The search for high-temperature superconductors in hydrides under high pressure has always been a research hotspot. Hydrogen-based superconductors offer an avenue to achieve the long-sought goal of superconductivity at room temperature. Here we systematically explored the high-pressure phase diagram, electronic properties, lattice dynamics and superconductivity of the ternary Ca-Al-H system using ab initio methods. At 80 GPa, CaAlH5 transforms from Cmcm to P21/m phase. Both of Cmcm-CaAlH5 and Pnnm-CaAl2H8 are semiconductors. At 200 GPa, P4/mmm-CaAlH7 and a metastable compound Immm-Ca2AlH12 were found. Furthermore, P4/mmm-CaAlH7 shows obvious softening of the high frequency vibration modes, which improves the strength of electron-phonon coupling. Therefore, a superconducting transition temperature Tc of 71 K is generated in P4/mmm-CaAlH7 at 50 GPa. In addition, the thermodynamic metastable Immm-Ca2AlH12 exhibits a superconducting transition temperature of 118 K at 250 GPa. These results are very useful for the experimental searching of new high-Tc superconductors in ternary hydrides. Our work may provide an opportunity to search for high Tc superconductors at lower pressure.
Arterial stiffness is a major contributor to morbidity and mortality worldwide. Although several metabolic markers associated with arterial stiffness have been developed, there is limited data regarding whether glycemic control modifies the association between diabetes and arterial stiffness. For these reasons, identification of traits around diabetes will directly contribute to arterial stiffness and atherosclerosis management in the context of predictive, preventive, and personalized medicine (PPPM). Thus, this study aimed to explore the relationship of diabetes and glycemic control status with arterial stiffness in a real-world setting. Data of participants from Beijing Xiaotangshan Examination Center (BXEC) with at least two surveys between 2008 and 2019 were used. Cumulative hazards were presented by inverse probability of treatment weighted (IPTW) Kaplan-Meier curves. Cox models were used to estimate the hazard ratio (HR) and 95% confidence interval (CI). Arterial stiffness was defined as brachial-ankle pulse wave velocity (baPWV) ≥1400 cm/s. Of 5837 participants, the mean baseline age was 46.5±9.3 years, including 3791 (64.9%) males. During a median follow-up of 4.0 years, 1928 (33.0%) cases of incident arterial stiffness were observed. People with diabetes at baseline had a 48.4% (HR: 1.484, 95% CI: 1.250–1.761) excessive risk of arterial stiffness. Adherence to good glycemic control attenuated the relationship between diabetes and arterial stiffness (HR: 1.264, 95% CI: 0.950–1.681); while uncontrolled diabetes was associated with the highest risk of arterial stiffness (HR: 1.629, 95% CI: 1.323–2.005). Results were consistent using IPTW algorithm and multiple imputed data. Our study quantified that diabetes status is closely associated with an increased risk of arterial stiffness and supported that adherence to good glycemic control could attenuate the adverse effect of diabetes on arterial stiffness. Therefore, glucose monitoring and control is a cost-effective strategy for the predictive diagnostics, targeted prevention, patient stratification, and personalization of medical services in early vascular damages and arterial stiffness.
Xianbei was one of the most powerful nomadic groups in Eastern Eurasia since the collapse of the Xiongnu empire. However, owing to a lack of first-hand written records, the origins of Xianbei and their relationships with surrounding populations remain enigmatic. Here, we produce genomic data of nine Xianbei individuals (ca. 200 CE to 300 CE) from northern China. By combining the available genomes in the literature, we assemble a database that covers almost the entire period of Xianbei as well as samples pre- and post-dating them, allowing us to set the Xianbei in a temporal context. Our study decisively addresses a longstanding hypothesis and supports that the Xianbei was originated from the Amur River region, more specifically from far northeastern China around the Great Khingan Mountain ranges. We also provide direct genetic evidence that during their initial process of moving southward toward the Central Plains of China, Xianbei only received limited exogenous genetic contribution from the local population they encountered, but after settling in northern China, Xianbei not only transformed from nomadic tribes to sedentary agriculturalists but also genetically admixed into the local residents there. In sum, our study represents the inaugural genomic exploration into the origins of the Xianbei, affirms the profound historical connection between the Xianbei and ancient Han Chinese communities, and elucidates the dynamic population history of northern China.
Metal-organic frameworks (MOFs) have shown significant potential as photocatalysts. It has been widely assumed that all catalytic active sites within MOFs are functional in photocatalytic reactions but for a three-dimensional MOF, whether the internal catalytic active sites can effectively absorb light and actively contribute to photocatalytic reactions remains to be explored. In this context, we synthesized a two-dimensional nanosheet MOF (2D-MOF) and a three-dimensional bulk MOF (3D-MOF) composed of Zr6 clusters and tetracarboxylic porphyrin (TCPP) by the approach described in the literature. Re(bpy)(CO)3Cl (bpy = 2,2′-bipyridine), which has remarkable CO2 photoreduction ability, was introduced to the two MOFs to create two new photocatalysts 2D-MOF-Re and 3D-MOF-Re, respectively. Photocatalytic CO2 reduction experiments show that based on the equal number of catalytic active sites, the CO turnover number (TON) of 2D-MOF-Re reaches 27.8 in 6 h, which is 50 times that of 3D-MOF-Re. The result shows that certain catalytic active sites inside the bulk MOF are inactive due to the inability to absorb light. This study illuminates the potential of the dimensional reduction approach in the design of photocatalysts to exploit the capabilities fully.
The coupling of different two‐dimensional materials (2DMs) to form van der Waals heterostructures (vdWHs) is a powerful strategy for adjusting the electronic properties of 2D semiconductors, for applications in opto‐electronics and quantum computing. 2D molybdenum disulfide (MoS 2 ) represents an archetypical semiconducting, monolayer thick versatile platform for the generation of hybrid vdWH with tunable charge transport characteristics through its interfacing with molecules and assemblies thereof. However, the physisorption of (macro)molecules on 2D MoS 2 yields hybrids possessing a limited thermal stability, thereby jeopardizing their technological applications. Herein, we report the rational design and optimized synthesis of 2D covalent organic frameworks (2D‐COFs) for the generation of MoS 2 / 2D‐COF vdWHs exhibiting strong interlayer coupling effects. The high crystallinity of the 2D‐COF films made it possible to engineer an ultrastable periodic doping effect on MoS 2 , boosting devices’ field‐effect mobility at room temperature. Such a performance increase can be attributed to the synergistic effect of the efficient interfacial electron transfer process and the pronounced suppression of MoS 2 ’s lattice vibration. This proof‐of‐concept work validates an unprecedented approach for the efficient modulation of the electronic properties of 2D transition metal dichalcogenides toward high‐performance (opto)electronics for CMOS digital circuits. This article is protected by copyright. All rights reserved
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