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Cadmium (Cd) has an inhibitory effect on the morphology, vegetative growth, and biochemical accumulation of plants including mungbean. The polyphenolic metabolites of the phenylpropanoid pathway, especially flavonoids, are known to promote Cd stress tolerance in plants. However, the potential of flavonoid precursor, naringenin for Cd tolerance has not been reported in mungbean. The present study documents the significant role of naringenin towards mungbean protein bioavailability and solubility. The protein accumulation, bioavailability, and solubility of Cd-stressed mungbean was increased by 20, 94, and 12 % respectively, on naringenin exposure. Exogenous naringenin alleviated the Cd toxicity-induced morphological and biochemical alterations of mungbean. The naringenin-mediated enhancement in chlorophyll was found to increase fresh biomass and carbohydrate accumulation of Cd-stressed mungbean by 53 and 0.9- 2.1 %. Furthermore, naringenin rescued the Cd reduced non-enzymatic antioxidant potential in terms of 30 and 32 % increment in the polyphenolics and flavonoids accumulation. However, naringenin reduced enzymatic antioxidants and enhanced the lipid peroxidation of mungbean. Therefore, in the prevailing condition of multiple and concomitant stresses induced by global climate change, seed treatment with phytochemicals like naringenin can be a novel strategy for heavy metal stress tolerance in legumes including mungbean.

The nitrogen (N) nutrition index (NNI) is a reliable indicator of crop N status and there is an urgent need to develop efficient technologies for non-destructive estimation of NNI to support the practical applications of precision N management strategies. The objectives of this study were to: (i) validate a newly established critical N dilution curve for spring maize in Northeast China; (ii) determine the potential of using the GreenSeeker active optical sensor to non-destructively estimate NNI; and (iii) evaluate the performance of different N status diagnostic approaches based on estimated NNI via the GreenSeeker sensor measurements. Four field experiments involving six N rates (0, 60, 120,180, 240, and 300 kg·ha−1) were conducted in 2014 and 2015 in Lishu County, Jilin Province in Northeast China. The results indicated that the newly established critical N dilution curve was suitable for spring maize N status diagnosis in the study region. Across site-years and growth stages (V5–V10), GreenSeeker sensor-based vegetation indices (VIs) explained 87%–90%, 87%–89% and 83%–84% variability of leaf area index (LAI), aboveground biomass (AGB) and plant N uptake (PNU), respectively. However, normalized difference vegetation index (NDVI) became saturated when LAI > 2 m2·m−2, AGB > 3 t·ha−1 or PNU > 80 kg·ha−1. The GreenSeeker-based VIs performed better for estimating LAI, AGB and PNU at V5–V6 and V7–V8 than the V9–V10 growth stages, but were very weakly related to plant N concentration. The response index calculated with GreenSeeker NDVI (RI–NDVI) and ratio vegetation index (R2 = 0.56–0.68) performed consistently better than the original VIs (R2 = 0.33–0.55) for estimating NNI. The N status diagnosis accuracy rate using RI–NDVI was 81% and 71% at V7–V8 and V9–V10 growth stages, respectively. We conclude that the response indices calculated with the GreenSeeker-based vegetation indices can be used to estimate spring maize NNI non-destructively and for in-season N status diagnosis between V7 and V10 growth stages under experimental conditions with variable N supplies. More studies are needed to further evaluate different approaches under diverse on-farm conditions and develop side-dressing N recommendation algorithms.

In view of the shortage of healthcare workers and a growing aging population, it is worthwhile to explore the applicability of new technologies in improving the quality of healthcare and reducing its cost. However, it remains a challenge to deploy such technologies in environments where individuals have limited knowledge about how to use them. Thus, this paper explores how the social robots designed for use in health settings in Australia have sought to overcome some of the limitations through personalization. Deployed in aged care and home-based care facilities, the social robots are person-centered, emphasizing the personalization of care with human-like attributes (e.g., human appearances) to engage in reciprocal communication with users. While there have been debates over the advantages and disadvantages of personalization, this paper discusses the implications of personalization on the design of the robots for enhancing engagement, empowerment and enablement in health settings.