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The precise and rapid estimation of leaf area index (LAI) is crucial for quantitative ecological monitoring and assessment, as it serves as an important ecological indicator of plant growth state and canopy structure. The acquisition, integration, and application of multiple remote sensing data from Unmanned Aerial Vehicle (UAV) platforms in quantitative ecological research is a current research hotspot. This study aims to explore the potential of combining morphological and spectral parameters using the symbolic regression (SR) algorithm with allometric models to improve the estimation of Leaf Area Index (LAI) at a regional scale of island ecosystem using UAV multi-source remote sensing data. The Canopy Height Model (CHM), Vegetation Density (VD), and Vegetation Indices (VIs), derived from UAV multi-platforms, were used as independent variables in the development of the retrieval models. Subsequently, regression equations were used to construct the Singlet, Duplet, and Triplet models. The precise assessments indicated that the triplet model demonstrates the highest accuracy, with a mean absolute error (MAE) of 9.48% and root mean square error (RMSE) of 0.2345. This improvement was observed compared to the singlet model (MAE = 19.55%, RMSE = 0.4091) and duplet model (MAE = 15.88%, RMSE = 0.3450), which can be attributed to incorporating morphological parameters to reduce the impact of tree height and leaf density. The analysis also showed that the estimated LAI accuracy is generally higher for shrubs and herbs compared to trees, and that lower plants with high vegetation coverage are better retrieved. The methodology advaneced in this study demonstrates significant potential and implications for ecological monitoring and assessment of island ecosystems, thereby expanding the research scope in the field of island ecology. Furthermore, it provides scientific evidence and technical support for decision-makers and scholars to propose and implement various measures for the conservation and restoration of island ecosystems. • Proposed a methodology of improving LAI estimation combining morphological and spectral parameters with symbolic regression. • Triplet model achieved highest accuracy, outperforming singlet and duplet models. • Higher LAI accuracy for shrubs and herbs compared to trees. • Implications for island ecological monitoring and ecosystem conservation. [ABSTRACT FROM AUTHOR]

Electrical safety is a major threat to public safety worldwide, with fires and explosions causing damage to property and loss of life. Detection and monitoring of the occurrence of destructive current spikes can provide valuable insights into system health and inform preventative maintenance. Current sensors based on the Magnetoelectric (ME) effect have been regarded as optimal solutions because of the low power consumption and high sensitivity of ME devices compared to conventional current sensors (current transformers, Hall-effect current sensors, etc.). However, none of the conventional sensing techniques can provide ex-situ recording or a means for a human-readable signal output. Development of a current sensor that can sense, visualize, and record a current level remains a challenge. Here, we report an electric current visualization sensor that uses a coupled magnetoelectric/electrochromic effect to transform environmental current energy into electrical signals, and subsequently to a color change readily interpreted by a human observer. The current visualization sensor uses a PZT piezoelectric microfiber composite (MFC) / FeSiB alloy (Metglas) laminate combined with a Prussian blue-based electrochromic device (ECD). Such a current sensor demonstrates high efficiency self-powered current sensing and non-volatile signal recording. [Display omitted] • An electric current visualization sensor that uses a coupled magnetoelectric-electrochromic effect has been designed. • The sensor has the ability to display the health status of electrical power system in an easy way to observe, nonvolatile display. • The current sensor demonstrates high efficiency self-powered current sensing and non-volatile signal recording. • The system is expected to provide new routes for health monitoring in power distribution systems. [ABSTRACT FROM AUTHOR]

The transmission and deposition of pathogenic bioaerosols and the subsequent contamination of the air and surfaces is well recognized as a potential route of hospital cross-infection. A full-scale experiment using Bacillus subtilis and computational fluid dynamics were utilized to model the bioaerosol characteristics in a two-bed hospital ward with a constant air change rate (12 ACH). The results indicated that the bioaerosol removal efficiency of unilateral downward ventilation was 50% higher than that of bilateral downward ventilation. Additionally, health care workers (HCWs) and nearby patients had lower breathing zone concentrations in the ward with unilateral downward ventilation. Furthermore, a partition played a positive role in protecting patients by reducing the amount of bioaerosol exposure. However, no obvious protective effect was observed with respect to the HCWs. Only 10% of the bioaerosol was deposited on the surfaces in the ward with unilateral downward ventilation, while up to 35% of the bioaerosol was deposited on the surfaces in the ward with bilateral downward ventilation during the 900 s. The main deposition locations of the bioaerosols were near the wall on the same side of the room as the patient's head in all cases. This study could provide scientific evidence for controlling cross-infection in hospital wards, as well as several guidelines for the disinfection of hospital wards. • Bioaerosol removal efficiency was quantitatively examined in ward with two types of typical downward ventilation. • Temporal and spatial distributions of bioaerosols in ward were determined in four cases. • Time-varying breathing zone concentrations of HCWs and nearby patients was quantitatively analyzed. • Effectiveness of partition in controlling spread of bioaerosols in the ward was explored. [ABSTRACT FROM AUTHOR]