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Palliative care is one of the effective means to protect the rights of good death and improve the quality of life in end-stage patients. Palliative care in Singapore is developing rapidly and a comprehensive palliative service system has been gradually established, ranking in the top of Asia in the latest national quality of death ranking. This paper proposes that the social awareness, education and training of palliative care should be strengthened, the power of charities and NGOs should be utilized, the existing palliative care carrying capacity should be improved, palliative care related system and legal protection should be established in China, by sorting out the current situation of hospice care, the practical experience in public education, personnel training, charitable support, related policies and legal protection in Singapore, in order to achieve the hospice needs of end-stage patients.

The in situ and ex situ temperature dependence of dielectric and piezoelectric properties of 0.1(Bi0.5K0.5)TiO3-0.6BiFeO3-0.3PbTiO3 (BKT-BFPT) ceramics were characterized, and related mechanisms were studied. The tetragonal distortion, Curie temperature Tc, depoling temperature Td, and piezoelectric coefficient d33 of BKT-BFPT are 0.06, 545 °C, 520 °C, and 205 pC/N, respectively. The in situ piezoelectric coefficient variation of BKT-BFPT ceramics from room temperature to 280 °C is only 10%, being 1/5 and 1/10 those of 0.36BiScO3-0.34PbTiO3 and Pb(Zr0.52Ti0.48)O3 ceramics. The better dielectric and piezoelectric thermal stabilities are owing to the temperature insensitive domain texture of BFPT-BKT ceramics due to their large lattice distortion. Resistivity results reflected that the higher depoling temperature derived from the ex situ measurement than that from the in situ test results from the high-temperature conductivity. [ABSTRACT FROM AUTHOR]

Magnetoelectric (ME) sensors are widely studied and well suited for current condition monitoring in smart grids due to their high sensitivity, low power consumption, and compact size. However, designing ME sensors that simultaneously achieve low magnetic noise and a large linear range remains challenging. In this work, we propose and systematically study a multilayered magnetoelectric sensor (MLMS). We experimentally demonstrate that the voltage noise is effectively suppressed by connecting the piezoelectric elements of the MLMS in series. Additionally, the magnetic flux concentration effect is weakened by magnetic shielding from the outer Metglas laminates, which increases the optimized bias field to 52 Oe for the MLMS. Consequently, an equivalent magnetic noise as low as 16.7 pT/rtHz at 1 Hz is obtained and an enlarged linear range from 20 pT to 2 mT is achieved. Furthermore, we demonstrate that the proposed MLMS could linearly detect a wide range of power currents from 0.1 mA to 400 A, with nonlinear error and current resolution as low as 0.05% and 0.1 mA, respectively. [ABSTRACT FROM AUTHOR]

Glaucoma is the leading cause of irreversible blindness worldwide. Often asymptomatic for years, this disease can progress significantly before patients become aware of the loss of visual function. Critical examination of the optic nerve through ophthalmoscopy or using fundus images is a crucial component of glaucoma detection before the onset of vision loss. The vertical cup-to-disc ratio (VCDR) is a key structural indicator for glaucoma, as thinning of the superior and inferior neuroretinal rim is a hallmark of the disease. However, manual assessment of fundus images is both time-consuming and subject to variability based on clinician expertise and interpretation. In this study, we develop a robust and accurate automated system employing deep learning (DL) techniques, specifically the YOLOv7 architecture, for the detection of optic disc and optic cup in fundus images and the subsequent calculation of VCDR. We also address the often-overlooked issue of adapting a DL model, initially trained on a specific population (e.g., European), for VCDR estimation in a different population. Our model was initially trained on ten publicly available datasets and subsequently fine-tuned on the REFUGE dataset, which comprises images collected from Chinese patients. The DL-derived VCDR displayed exceptional accuracy, achieving a Pearson correlation coefficient of 0.91 (P = 4.12 × 10–412) and a mean absolute error (MAE) of 0.0347 when compared to assessments by human experts. Our models also surpassed existing approaches on the REFUGE dataset, demonstrating higher Dice similarity coefficients and lower MAEs. Moreover, we developed an optimization approach capable of calibrating DL results for new populations. Our novel approaches for detecting optic discs and optic cups and calculating VCDR, offers clinicians a promising tool that significantly reduces manual workload in image assessment while improving both speed and accuracy. Most importantly, this automated method effectively differentiates between glaucoma and non-glaucoma cases, making it a valuable asset for glaucoma detection. [ABSTRACT FROM AUTHOR]