Analysis of the Electrical and Thermal Properties for Magnetic Fe3O4-Coated SiC-Filled Epoxy Composites.

Orderly arranged Silicon carbide (SiC)/epoxy (EP) composites were fabricated. SiC was made magnetically responsive by decorating the surface with iron oxide (Fe3O4) nanoparticles. Three treatment methods, including without magnetization, pre-magnetization and curing magnetization, were used to prepare SiC/EP composites with different filler distributions. Compared with unmodified SiC, magnetic SiC with core-shell structure was conducive to improve the breakdown strength of SiC/EP composites and the maximum enhancement rate was 20.86%. Among the three treatment methods, SiC/EP composites prepared in the curing-magnetization case had better comprehensive properties. Under the action of magnetic field, magnetic SiC were orderly oriented along the direction of an external field, thereby forming SiC chains. The magnetic alignment of SiC restricted the movement of EP macromolecules or polar groups to some extent, resulting in the decrease in the dielectric constant and dielectric loss. The SiC chains are equivalent to heat flow channels, which can improve the heat transfer efficiency, and the maximum improvement rate was 23.6%. The results prove that the orderly arrangement of SiC had a favorable effect on dielectric properties and thermal conductivity of SiC/EP composites. For future applications, the orderly arranged SiC/EP composites have potential for fabricating insulation materials in the power electronic device packaging field

Quantum Neuronal Sensing of Quantum Many-Body States on a 61-Qubit Programmable Superconducting Processor

Classifying many-body quantum states with distinct properties and phases of matter is one of the most fundamental tasks in quantum many-body physics. However, due to the exponential complexity that emerges from the enormous numbers of interacting particles, classifying large-scale quantum states has been extremely challenging for classical approaches. Here, we propose a new approach called quantum neuronal sensing. Utilizing a 61 qubit superconducting quantum processor, we show that our scheme can efficiently classify two different types of many-body phenomena: namely the ergodic and localized phases of matter. Our quantum neuronal sensing process allows us to extract the necessary information coming from the statistical characteristics of the eigenspectrum to distinguish these phases of matter by measuring only one qubit. Our work demonstrates the feasibility and scalability of quantum neuronal sensing for near-term quantum processors and opens new avenues for exploring quantum many-body phenomena in larger-scale systems.Comment: 7 pages, 3 figures in the main text, and 13 pages, 13 figures, and 1 table in supplementary material

Experimental quantum computational chemistry with optimised unitary coupled cluster ansatz

Simulation of quantum chemistry is one of the most promising applications of quantum computing. While recent experimental works have demonstrated the potential of solving electronic structures with variational quantum eigensolver (VQE), the implementations are either restricted to nonscalable (hardware efficient) or classically simulable (Hartree-Fock) ansatz, or limited to a few qubits with large errors for the more accurate unitary coupled cluster (UCC) ansatz. Here, integrating experimental and theoretical advancements of improved operations and dedicated algorithm optimisations, we demonstrate an implementation of VQE with UCC for H_2, LiH, F_2 from 4 to 12 qubits. Combining error mitigation, we produce high-precision results of the ground-state energy with error suppression by around two orders of magnitude. For the first time, we achieve chemical accuracy for H_2 at all bond distances and LiH at small bond distances in the experiment. Our work demonstrates a feasible path towards a scalable solution to electronic structure calculation, validating the key technological features and identifying future challenges for this goal.Comment: 8 pages, 4 figures in the main text, and 29 pages supplementary materials with 16 figure

Translating Research Evidence Into Marketplace Application: Cohort Study of Internet-Based Intervention Platforms for Perinatal Depression

BackgroundInternet-based intervention platforms may improve access to mental health care for women with perinatal depression (PND). Though the majority of platforms in the market lack an evidence base, a small number of them are supported by research evidence. ObjectiveThis study aims to assess the current status of internet-based PND intervention platforms supported by published evidence, understand the reasons behind the disappearance of any of these previously accessible platforms, examine adjustments made by those active platforms between research trials and market implementation, and evaluate their current quality. MethodsA cohort of internet-based PND intervention platforms was first identified by systematic searches in multiple academic databases from database inception until March 26, 2021. We searched on the World Wide Web and the iOS and Android app stores to assess which of these were available in the marketplace between April and May 2021. The basic characteristics of all platforms were collected. For inaccessible platforms, inquiries were made via email to the authors of publications to determine the reasons for their unavailability. We compared the intervention-related information of accessible platforms in the marketplace with that reported in original publications and conducted quality assessments using the App Evaluation Model of the American Psychiatric Association. Fisher exact tests were used to compare the functional characteristics in publications of available and unavailable platforms and to investigate potential associations between functional adjustments or quality indices and platform survival time. ResultsOut of 35 platforms supported by research evidence, only 19 (54%) were still accessible in the marketplace. The main reason for platforms disappearing was the termination of research projects. No statistically significant differences were found in functional characteristics between available and unavailable platforms. A total of 18 (95%) platforms adapted their core functions from what was reported in related publications. The adjustments included changes to intervention methods (11/19, 58%), target population (10/19, 53%), human resources for intervention support (9/19, 47%), mood assessment and monitoring (8/19, 42%), communication modality (4/19, 21%), and platform type (2/19, 11%). Quality issues across platforms included low frequency of update, lack of crisis management mechanism, poor user interactivity, and weak evidence base or absence of citation of supporting evidence. Platforms that survived longer than 10 years had a higher tendency to use external resources from third parties compared to those that survived less than 10 years (P=.04). No significant differences were found for functional adjustments or other quality indices. ConclusionsInternet-based platforms supported by evidence were not effectively translated into real-world practice. It is unclear if adjustments to accessible platforms made during actual operation may undermine the proven validity of the original research. Future research to explore the reasons behind the success of the implementation of evidence-based platforms in the marketplace is warranted

Improving Accuracy of Real-Time Positioning and Path Tracking by Using an Error Compensation Algorithm against Walking Modes

Wide-range application scenarios, such as industrial, medical, rescue, etc., are in various demand for human spatial positioning technology. However, the existing MEMS-based sensor positioning methods have many problems, such as large accuracy errors, poor real-time performance and a single scene. We focused on improving the accuracy of IMU-based both feet localization and path tracing, and analyzed three traditional methods. In this paper, a planar spatial human positioning method based on high-resolution pressure insoles and IMU sensors was improved, and a real-time position compensation method for walking modes was proposed. To validate the improved method, we added two high-resolution pressure insoles to our self-developed motion capture system with a wireless sensor network (WSN) system consisting of 12 IMUs. By multi-sensor data fusion, we implemented dynamic recognition and automatic matching of compensation values for five walking modes, with real-time spatial-position calculation of the touchdown foot, enhancing the 3D accuracy of its practical positioning. Finally, we compared the proposed algorithm with three old methods by statistical analysis of multiple sets of experimental data. The experimental results show that this method has higher positioning accuracy in real-time indoor positioning and path-tracking tasks. The methodology can have more extensive and effective applications in the future

Effect of Working Temperature Conditions on the Autogenous Deformation of High-Performance Concrete Mixed with MgO Expansive Agent

Currently, mass concrete is increasingly utilized in various engineering projects that demand high physical properties of concrete. The water-cement ratio of mass concrete is comparatively smaller than that of the concrete used in dam engineering. However, the occurrence of severe cracking in mass concrete has been reported in numerous engineering applications. To address this issue, the incorporation of MgO expansive agent (MEA) in concrete has been widely recognized as an effective method to prevent mass concrete from cracking. In this research, three distinct temperature conditions were established based on the temperature elevation of mass concrete in practical engineering scenarios. To replicate the temperature increase under operational conditions, a device was fabricated that employed a stainless-steel barrel as the container for concrete, which was enveloped with insulation cotton for thermal insulation purposes. Three different MEA dosages were used during the pouring of concrete, and sine strain gauges were placed within the concrete to gauge the resulting strain. The hydration level of MEA was studied using thermogravimetric analysis (TG) to calculate the degree of hydration. The findings demonstrate that temperature has a significant impact on the performance of MEA; a higher temperature results in more complete hydration of MEA. The design of the three temperature conditions revealed that when the peak temperature exceeded 60 °C in two cases, the addition of 6% MEA was sufficient to fully compensate for the early shrinkage of concrete. Moreover, in instances where the peak temperature exceeded 60 °C, the impact of temperature on accelerating MEA hydration was more noticeable

Tumor associated macrophages in esophageal squamous carcinoma: Promising therapeutic implications

Esophageal squamous carcinoma (ESCC) is a prevalent and highly lethal malignant tumor, with a five-year survival rate of approximately 20 %. Tumor-associated macrophages (TAMs) are the most prominent immune cells in the tumor microenvironment (TME), comprising over 50 % of the tumor volume. TAMs can be polarized into two distinct phenotypes, M1-type and M2-type, through interactions with cancer cells. M2-type TAMs are more abundant than M1-type TAMs in the TME, contributing to tumor progression, such as tumor cell survival and the construction of an immunosuppressive environment. This review focuses on the role of TAMs in ESCC, including their polarization, impact on tumor proliferation, angiogenesis, invasion, migration, therapy resistance, and immunosuppression. In addition, we discuss the potential of targeting TAMs for clinical therapy in ESCC. A thorough comprehension of the molecular biology about TAMs is essential for the development of innovative therapeutic strategies to treat ESCC