Electron-Spin Excitation Coupling in an Electron Doped Copper Oxide Superconductor

High-temperature (high-Tc) superconductivity in the copper oxides arises from electron or hole doping of their antiferromagnetic (AF) insulating parent compounds. The evolution of the AF phase with doping and its spatial coexistence with superconductivity are governed by the nature of charge and spin correlations and provide clues to the mechanism of high-Tc superconductivity. Here we use a combined neutron scattering and scanning tunneling spectroscopy (STS) to study the Tc evolution of electron-doped superconducting Pr0.88LaCe0.12CuO4-delta obtained through the oxygen annealing process. We find that spin excitations detected by neutron scattering have two distinct modes that evolve with Tc in a remarkably similar fashion to the electron tunneling modes in STS. These results demonstrate that antiferromagnetism and superconductivity compete locally and coexist spatially on nanometer length scales, and the dominant electron-boson coupling at low energies originates from the electron-spin excitations.Comment: 30 pages, 12 figures, supplementary information include

Small Fermi pockets intertwined with charge stripes and pair density wave order in a kagome superconductor

The kagome superconductor family AV3Sb5 (A=Cs, K, Rb) emerged as an exciting platform to study exotic Fermi surface instabilities. Here we use spectroscopic-imaging scanning tunneling microscopy (SI-STM) and angle-resolved photoemission spectroscopy (ARPES) to reveal how the surprising cascade of higher and lower-dimensional density waves in CsV3Sb5 is intimately tied to a set of small reconstructed Fermi pockets. ARPES measurements visualize the formation of these pockets generated by a 3D charge density wave transition. The pockets are connected by dispersive q* wave vectors observed in Fourier transforms of STM differential conductance maps. As the additional 1D charge order emerges at a lower temperature, q* wave vectors become substantially renormalized, signaling further reconstruction of the Fermi pockets. Remarkably, in the superconducting state, the superconducting gap modulations give rise to an in-plane Cooper pair-density-wave at the same q* wave vectors. Our work demonstrates the intrinsic origin of the charge-stripes and the pair-density-wave in CsV3Sb5 and their relationship to the Fermi pockets. These experiments uncover a unique scenario of how Fermi pockets generated by a parent charge density wave state can provide a favorable platform for the emergence of additional density waves

CMRxRecon: An open cardiac MRI dataset for the competition of accelerated image reconstruction

Cardiac magnetic resonance imaging (CMR) has emerged as a valuable diagnostic tool for cardiac diseases. However, a limitation of CMR is its slow imaging speed, which causes patient discomfort and introduces artifacts in the images. There has been growing interest in deep learning-based CMR imaging algorithms that can reconstruct high-quality images from highly under-sampled k-space data. However, the development of deep learning methods requires large training datasets, which have not been publicly available for CMR. To address this gap, we released a dataset that includes multi-contrast, multi-view, multi-slice and multi-coil CMR imaging data from 300 subjects. Imaging studies include cardiac cine and mapping sequences. Manual segmentations of the myocardium and chambers of all the subjects are also provided within the dataset. Scripts of state-of-the-art reconstruction algorithms were also provided as a point of reference. Our aim is to facilitate the advancement of state-of-the-art CMR image reconstruction by introducing standardized evaluation criteria and making the dataset freely accessible to the research community. Researchers can access the dataset at https://www.synapse.org/#!Synapse:syn51471091/wiki/.Comment: 14 pages, 8 figure

Benchmark study of run-to-run controllers for the lithographic control of the critical dimension

The article of record as published may be found at http://dx.doi.org/10.1117/1.2743657We present a systematic robustness analysis for several feedback controllers used in photolithographic critical dimension CD control in semiconductor manufacturing. Our study includes several controllers based on either the exponentially weighted moving average EWMA estimation or Kalman filters. The robustness is characterized by two features, namely the controller’s stability margin in the presence of model mismatch and the controller’s sensitivity to unknown noise. Simulations on the closed-loop control system are shown for the performance comparison. Both the analysis and the simulations prove that the multiple-dimensional feedback controller developed in this paper using the average of previous inputs and outputs outperforms the other controllers in the group

A three tier cooperative control architecture for multi-step semiconductor manufacturing proces

The article of record as published may be found at http://dx.doi.org/10.1016/j.jprocont.2008.04.003In this paper, cooperative control is investigated and applied to chained processes with multiple steps and multiple tools in semiconductor manufacturing. A cooperative control architecture is proposed to optimize product quality, to improve yield, to achieve best tool performance, and to minimize throughput time. The architecture consists of three tiers: the top tier for target optimization and overall product performance, the middle tier for tool selection based on tool performance, throughput time and tool availability, and the bottom tier for tool level run-to-run control. Large data sets are collected from four individual process steps in a fabrication facility of a leading semiconductor manufacturer and the data sets are processed and lined up for the study of cooperative control. Monte Carlo simulations are carried out based on the real data to demonstrate a significant improvement for the end-of-line product qualit

Effectiveness of artificial intelligence methods in personalized aggression risk prediction within inpatient psychiatric treatment settings - a systematic review

Aggression risk assessments are vital to prevent injuries and morbidities amongst patients and staff in psychiatric settings. More recent studies have harnessed artificial intelligence (AI) methods such as machine learning algorithms to determine factors associated with aggression in psychiatric treatment settings. In this review, using Cooper's five-stage review framework, we aimed to evaluate the: (1) predictive accuracy, and (2) clinical variables associated with AI-based aggression risk prediction amongst psychiatric inpatients. Databases including PubMed, Cochrane, Scopus, PsycINFO, CINAHL were searched for relevant articles until April 2022. The eight included studies were independently evaluated using critical appraisal tools for systematic review developed by Joanna Briggs Institute. Most of the studies (87.5%) examined health records in predicting aggression and reported acceptable to excellent accuracy with specific machine learning algorithms employed (area under curve range 0.75-0.87). No particular machine learning algorithm outperformed the others consistently across studies (area under curve range 0.61-0.87). Relevant factors identified with aggression related to demographic and social profile, past aggression, forensic history, other psychiatric history, psychopathology, challenging behaviors and management domains. The limited extant studies have highlighted a potential role for the use of AI methods to clarify factors associated with aggression in psychiatric inpatient treatment settings.Published versionThe study was funded by West Region, Institute of Mental Health

Modeling of Time Geographical Kernel Density Function under Network Constraints

Time geography considers that the probability of moving objects distributed in an accessible transportation network is not always uniform, and therefore the probability density function applied to quantitative time geography analysis needs to consider the actual network constraints. Existing methods construct a kernel density function under network constraints based on the principle of least effort and consider that each point of the shortest path between anchor points has the same density value. This, however, ignores the attenuation effect with the distance to the anchor point according to the first law of geography. For this reason, this article studies the kernel function framework based on the unity of the principle of least effort and the first law of geography, and it establishes a mechanism for fusing the extended traditional model with the attenuation model with the distance to the anchor point, thereby forming a kernel density function of time geography under network constraints that can approximate the theoretical prototype of the Brownian bridge and providing a theoretical basis for reducing the uncertainty of the density estimation of the transportation network space. Finally, the empirical comparison with taxi trajectory data shows that the proposed model is effective

Modeling of Time Geographical Kernel Density Function under Network Constraints

Time geography considers that the probability of moving objects distributed in an accessible transportation network is not always uniform, and therefore the probability density function applied to quantitative time geography analysis needs to consider the actual network constraints. Existing methods construct a kernel density function under network constraints based on the principle of least effort and consider that each point of the shortest path between anchor points has the same density value. This, however, ignores the attenuation effect with the distance to the anchor point according to the first law of geography. For this reason, this article studies the kernel function framework based on the unity of the principle of least effort and the first law of geography, and it establishes a mechanism for fusing the extended traditional model with the attenuation model with the distance to the anchor point, thereby forming a kernel density function of time geography under network constraints that can approximate the theoretical prototype of the Brownian bridge and providing a theoretical basis for reducing the uncertainty of the density estimation of the transportation network space. Finally, the empirical comparison with taxi trajectory data shows that the proposed model is effective

A Novel High-Speed Permanent Magnet Synchronous Motor for Hydrogen Recirculation Side Channel Pumps in Fuel Cell Systems

In hydrogen recirculation side channel pumps, the motor rotor is exposed to a high-pressure mixture of steam and hydrogen, which makes hydrogen embrittlement occur in permanent magnets (PMs). A protective coating is necessary for the PMs in high-pressure hydrogen. However, in the process of sleeve interference installation, the protective coating of the PMs is easily damaged. This paper proposes two surface-mounted insert permanent magnet (SIPM) synchronous motor topologies, SIPM1 and SIPM2, in which the retaining sleeves can be eliminated and the PM protective coating is safe in the assembling process. A dovetail PM and rotor core structure is used to protect the PM with higher rotor strength without retaining the sleeve. The electromagnetic performance of the motors with different rotors, including airgap flux density, output torque, torque ripple, and energy efficiency is compared and optimized. It is concluded that the output torque of the SIPM motor can be promoted by 22.4% and torque ripple can be reduced by 2.9%, while the PM volume remains the same as that of the conventional SPM motor. At the same time, the SIPM motor can have lower harmonic contents of back electromotive force (EMF) and rotor loss compared to the SPM motor with a retaining sleeve. Furthermore, the stress of the PM is analyzed under conditions of PM glue action and failure. The proposed SIPM2 has the ability to operate safely at high-speed and high-temperature operating conditions when the PM glue fails

Reestablish immune tolerance in rheumatoid arthritis.

Rheumatoid arthritis (RA) is a chronic progressive autoimmune disease. Despite the wide use of conventional synthetic, targeted and biologic disease modifying anti-rheumatic drugs (DMARDs) to control its radiological progress, nearly all DMARDs are immunologically non-selective and do not address the underlying immunological mechanisms of RA. Patients with RA often need to take various DMARDs long-term or even lifelong and thus, face increased risks of infection, tumor and other adverse reactions. It is logical to modulate the immune disorders and restore immune balance in patients with RA by restoring immune tolerance. Indeed, approaches based on stem cell transplantation, tolerogenic dendritic cells (tolDCs), and antigen-based tolerogenic vaccination are under active investigation, and some have already transformed from wet bench research to clinical investigation during the last decade. Among them, clinical trials on stem cell therapy, especially mesenchymal stem cells (MSCs) transplantation are most investigated and followed by tolDCs in RA patients. On the other hand, despite active laboratory investigations on the use of RA-specific peptide-/protein-based tolerogenic vaccines for T cell, clinical studies on RA patients are much limited. Overall, the preliminary results of these clinical studies are promising and encouraging, demonstrating their safety and effectiveness in the rebalancing of T cell subsets; particular, the recovery of RA-specific Treg with increasing anti-inflammatory cytokines and reduced proinflammatory cytokines. Future studies should focus on the optimization of transplanted stem cells, the preparation of tolDCs, and tolerogenic vaccines with RA-specific protein or peptide, including their dosage, course, and route of administration with well-coordinated multi-center randomized clinical control researches. With the progress of experimental and clinical studies, generating and restoring RA-specific immune tolerance may bring revolutionary changes to the clinical management of RA in the near future