Background: Health care professionals are required to maintain accurate health records of patients. Furthermore, these records should be shared across different health care organizations for professionals to have a complete review of medical history and avoid missing important information. Nowadays, health care providers use electronic health records (EHRs) as a key to the implementation of these goals and delivery of quality care. However, there are technical and legal hurdles that prevent the adoption of these systems, such as concerns about performance and privacy issues. Objective: This study aimed to build and evaluate an experimental blockchain for EHRs, named HealthChain, which overcomes the disadvantages of traditional EHR systems. Methods: HealthChain is built based on consortium blockchain technology. Specifically, three organizations, namely hospitals, insurance providers, and governmental agencies, form a consortium that operates under a governance model, which enforces the business logic agreed by all participants. Every peer node hosts an instance of the distributed ledger consisting of EHRs and an instance of chaincode regulating the permissions of participants. Designated orderers establish consensus on the order of EHRs and then disseminate blocks to peers. Results: HealthChain achieves functional and nonfunctional requirements. It can store EHRs in a distributed ledger and share them among different participants. Moreover, it demonstrates superior features, such as privacy preservation, security, and high throughput. These are the main reasons why HealthChain is proposed. Conclusions: Consortium blockchain technology can help to build new EHR systems and solve the problems that prevent the adoption of traditional systems.

We propose a realistic physical scheme to realize linear Gaussian optical potential with parity-time (PT) symmetry and two dimensional (2D) spacial solitons in a coherent atomic gas. It is shown that the PT-symmetric potential can be created through the spatial modulation of the control and relevant atomic parameters. We find that the Gaussian PT potential parameters, the imaginary part and the width and the position, play crucial roles in the occurrence of the PT phase transition. We demonstrate that the system supports stable 2D dipole solitons and vortex solitons, which can be managed via tuning PT potential. Furthermore, the dynamic characteristics of the symmetric scatter and collision of solitons are shown.

We explored exceptional points (EPs) in one dimensional non-Hermitian photonic crystals incorporated with a defect. The defect was asymmetric with respect to the center. Two EPs could be derived by modulating the normalized frequency and the gain-loss coefficient of defect. The reflection coefficient complex phase changed dramatically around EPs, and the change in complex phase was π at EPs. The electric field of EPs was mainly restricted to the defect, which can induce a giant Goos–Hänchen (GH) shift. Moreover, we found a coherent perfect absorption-laser point (CPA-LP) in the structure. A giant GH shift also existed around the CPA-LP. The study may have found applications in highly sensitive sensors.

We explore the optical bistability in aperiodic parity–time-symmetric (PT-symmetric) photonic lattices that are composed of Fibonacci sequence dielectrics and graphene at terahertz frequencies. Two Fibonacci sequence dielectrics, viz. aperiodic photonic lattices, are utilized for enhancing band-edge resonances and achieving the electric field localization that can enhance the nonlinearity of graphene. Modulating the gain-loss factor of dielectrics in the PT symmetry lattices further strengthens the nonlinearity effect and, consequently, low threshold bistability is realized. The interval between the upper and lower bistability thresholds enlarges as the momentum relaxation time of graphene changes. Moreover, we show that the bistability threshold can also be flexibly tuned by modulating the graphene chemical potential. The study might be applied in photomemories and optical switches.

The optical bistability, tristability and multistability are explored in arrays of graphene. The arrays are periodically arranged spatially by single sheets of graphene. Optical bistability could be achieved with a strong enough incident intensity of light wave. The thresholds of optical bistability and the intervals between the upper and lower thresholds change with the surface conductivity of graphene and the incident wavelength. By increasing the intensity of incident light, tristability and multistability can be induced as well. Furthermore, the thresholds of bistability, tristability and multistability can be regulated via the chemical potential of graphene. This study may have potential applications in optical logic gates, all-optical switches and photomemory.

Non-linear least squares problems arise from data fitting have received recently a lot of attention, particularly for the estimates of the model parameters over networked systems. Although the diffusion Gauss-Newton method offers many advantages for solving the non-linear least squares problem in wireless sensor network to estimate target position parameter, there are some key challenges when applying it to practice, including singularity of Gauss-Newton Hessian, selection to constant step sizes and steady state oscillation. These remaining issues lead to obvious performance degradation such as high computational cost, vulnerability to step size change and resulting instability on estimation.In this paper, to eliminate the singularity, we develop a diffusion Levenberg-Marquardt method such that the problem of constant step size is also addressed together. Then, to reach agreement on estimated vector, a consensus implementation is further proposed, thus eliminating the oscillation during steady state. Consequently, the proposed consensus-based diffusion Levenberg-Marquardt method provides a general solution for the non-linear least squares problems with an objective that takes the form of a sum of squared residual terms. By applying to collaborative localization and distributed optimization arise in large scale machine learning, simulation results confirm the effectiveness and wide applicability of proposed method in terms of convergence rate, accuracy and consistency of estimates.