An enhanced grey wolf optimizer boosted machine learning prediction model for patient-flow prediction.

Large and medium-sized general hospitals have adopted artificial intelligence big data systems to optimize the management of medical resources to improve the quality of hospital outpatient services and decrease patient wait times in recent years as a result of the development of medical information technology and the rise of big medical data. However, owing to the impact of several elements, including the physical environment, patient, and physician behaviours, the real optimum treatment effect does not meet expectations. In order to promote orderly patient access, this work provides a patient-flow prediction model that takes into account shifting dynamics and objective rules of patient-flow to handle this issue and forecast patients' medical requirements. First, we propose a high-performance optimization method (SRXGWO) and integrate the Sobol sequence, Cauchy random replacement strategy, and directional mutation mechanism into the grey wolf optimization (GWO) algorithm. The patient-flow prediction model (SRXGWO-SVR) is then proposed using SRXGWO to optimize the parameters of support vector regression (SVR). Twelve high-performance algorithms are examined in the benchmark function experiments' ablation and peer algorithm comparison tests, which are intended to validate SRXGWO's optimization performance. In order to forecast independently in the patient-flow prediction trials, the data set is split into training and test sets. The findings demonstrated that SRXGWO-SVR outperformed the other seven peer models in terms of prediction accuracy and error. As a result, SRXGWO-SVR is anticipated to be a reliable and efficient patient-flow forecast system that may help hospitals manage medical resources as effectively as possible.

16 × 16 non-blocking silicon optical switch based on electro-optic Mach-Zehnder interferometers.

We experimentally demonstrate a 16 × 16 non-blocking optical switch fabric with a footprint of 10.7 × 4.4 mm2. The switch fabric is composed of 56 2 × 2 silicon Mach-Zehnder interferometers (MZIs), with each integrated with a pair of TiN resistive micro-heaters and a p-i-n diode. The average on-chip insertion loss at 1560 nm wavelength is ~6.7 dB and ~14 dB for the "all-cross" and "all-bar" states, respectively, with a loss variation of ± 1 dB over all routing paths. The measured rise/fall time of the switch upon electrical tuning is 3.2/2.5 ns. The switching functionality is verified by transmission of 20 Gb/s on-off keying (OOK) and 50 Gb/s quadrature phase-shift keying (QPSK) optical signals.

Seven-bit reconfigurable optical true time delay line based on silicon integration.

We design, fabricate, and characterize a 7-bit reconfigurable optical true time delay line consisting of Mach-Zehnder interferometer (MZI) switches on the silicon photonics platform. Variable optical attenuators (VOAs) are embedded to suppress the inter-symbol crosstalk caused by the finite extinction ratio of switches. The device can provide a maximum of 1.27 ns delay with a 10 ps resolution over a wide wavelength range. Eye diagram measurement of a 25 Gbps 2(51)-1 pseudo-random bit sequence (PRBS) signal reveals the power penalties only increase 0.17 dB and 0.77 dB after transmission through the shortest (reference) and the longest (1.27 ns delay) paths, respectively.