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Objective. The aim of this study is to investigate continuous blood pressure waveform estimation from a plethysmography (PPG) signal, thus providing more human cardiovascular status information than traditional cuff-based methods. Approach. The proposed method utilizes the feature extraction ability of a convolution neural network to estimate blood pressure (BP) from PPG signals without the need for waveform analysis and signal feature extraction. Main results. The network achieved mean absolute errors and standard deviations of 2.55 ± 3.92 mmHg for systolic BP (SBP), 1.66 ± 2.76 mmHg for diastolic BP (DBP), and 2.52 ± 3.02 mmHg for overall pressure waveform. The results meet the best levels of the protocols of the British Hypertension Society (BHS) and the Association for the Advancement of Medical Instrumentation (AAMI). Significance. The proposed method shows promise for noninvasive continuous BP monitoring in hospital wards and daily life, which can assist in clinical diagnosis, disease treatment, and rehabilitation.

Crane operator training is an essential part of construction safety and is attracting extensive attention from researchers worldwide. Virtual reality (VR) is considered an effective tool to improve training outcomes by providing users with an immersive, risk-free experience in various environments. However, previous VR-based training platforms mainly focused on the scenario and task design; few studies attempted to investigate the impact of simulation fidelity on training efficiency. This research aims to explore the effect of simulation fidelity on training outcomes by comparing user performance in two scenarios. A typical construction site was modelled in a game engine using two rendering approaches; an eye-tracking system was adopted for data collection. The results from a subject experiment indicated the high efficiency of VR in operator safety training and demonstrated the usefulness of eye-tracking in measuring hazard detection performance. Findings showed that a higher level of simulation fidelity might not significantly improve the training efficiency, especially in hazard detection aspects.

Acoustic focusing has been widely applied in biological and industrial fields. In this work, a coding acoustic metasurface consisting of two kinds of hexagonal coding bits is designed. Using the metasurface, acoustic focusing can be implemented in three-dimensional space. Besides, by altering the coding sequence, the focal length can be manipulated flexibly to satisfy the practical demands. Furthermore, bifocal focusing, which has a great potential in multiplane imaging, can be realized by properly arranging the coding sequence. Our works broaden the prospects of the coding metasurfaces and have promising applications in the areas of biomedical therapy and imaging.

Accurate measurement of soil moisture parameters plays a vital role in promoting agricultural production. Nuclear magnetic resonance (NMR) measuring instruments have become increasingly compact, especially in low field applications. However, NMR soil moisture measurement instruments still have problems with portability. We report on a compact NMR measuring instrument that focuses on the circuit’s design and construction. The instrument includes an inside-out sensor, NMR spectrometer, radio frequency power amplifier, low-noise preamplifier, and duplexer. They are integrated into a slim 3D-printed cylinder. The integrated NMR instrument is portable and user-friendly and can reduce the influence of nonideal factors, such as transmission line effects and electromagnetic interference. We conducted different soil moisture experiments with the prototype using Carr–Purcell–Meiboom–Gill pulse sequence to obtain the corresponding transverse relaxation time (T 2) decay curves. Then, the T 2 1D spectrum distributions were obtained by inverse Laplace transformation. Finally, the results reveal that the area of long T 2 component decreases as the soil moisture content decreases.

With the proliferation of the distributed energy resources (DERs), the scheduling and control of the distribution network have become more complicated. To cope with the uncertainty nature of distributed generation, a multi-timescale optimal dispatch method in active distribution network (ADN) based on the model predictive control (MPC) is proposed in this paper. First, based on MPC, a hierarchical scheduling framework for ADN is established, including long-timescale stage, and short-timescale stage. Then, via coordinated control of various resources in the ADN, i.e., distributed generators, energy storage, capacitor banks and OLTC transformer, the impact of intermittent renewable energy and load forecast errors can be reduced. Finally, considering the coupling characteristics of active and reactive power in the ADN, a joint active and reactive power optimization model is proposed to further reduce the network loss. Numerical simulation on a modified IEEE-33 distribution network system verifies the correctness and superiority of the proposed scheduling approach.