An ethnobotanical study of wild edible plants used by the Tibetan in the Rongjia River Valley, Tibet, China.

BACKGROUND: Wild edible plants (WEPs) play a crucial role in communities with limited communication with the outside world, where unstable factors, such as poor food supply and insufficient access to timely nutritional supplementation, are common, as in the Himalayan region. To document the traditional knowledge of WEPs and explore their significance for communities with minimal global economic exchange, an ethnobotanical study was conducted in the town of Rongjia, which lies in a narrow valley near Mount Everest, Tibet, China. METHODS: This ethnobotanical study was conducted in three villages in the Rongjia River Valley between August 2021 and June 2023. Semi-structured interviews and participatory observations were used to collect information on WEPs. The fieldwork was performed with the assistance of local guides. Voucher specimens were collected from each documented plant species for taxonomic identification. We used the use report (UR) and relative frequency of citations (RFC) to evaluate the comprehensive utilization value of WEPs. RESULTS: We interviewed 161 informants who provided us with 2499 use reports. We collected 50 WEPs belonging to 28 families and 42 genera used by the Tibetan people in the Rongjia River Valley. WEPs are used in vegetables, fruits, seasonings, healthcare foods, substitute grains, and beverages. Wild vegetables were the most commonly used, followed by wild fruits. Leaves were the most commonly consumed part of the plant. The three most important WEPs ordered by RFC values were Rosa sericea var. glandulosa Osmaston (RFC = 0.76), Zanthoxylum bungeanum Maxim. (RFC = 0.75), and Urtica hyperborea Jacquem. ex Wedd. (RFC = 0.71). Other than that, we also document some of WEPs used in the past. Arisaema erubescens Schott, Pinellia ternata (Thunb.) Makino, and Satyrium nepalense var. ciliatum (Lindl.) Hook. f. used to serve as important substitute grains, are no longer in use, however, they remain vivid in the memories of older people. CONCLUSIONS: WEPs included wild vegetables, fruits, seasonings, healthcare food, and substitute grains for Tibetan people in the Rongjia River Valley. Some WEPs have become important cultural symbols for older people, which can help in understanding the relationship between plants and local people in the past. In addition, WEPs can increase the resilience of local people living in remote areas when facing sudden destabilizing events in future. This is the significance of WEPs for communities with minimal global economic exchange. Therefore, we suggest that future studies focus more on WEPs in communities with limited communication with the world to improve their resilience.

Enhanced energy recovery from landfill leachate by linking light and dark bio-reactions: Underlying synergistic effects of dual microalgal interaction.

Bioconversion of nutrients and energy from landfill leachate (LL) to biohydrogen and volatile fatty acids (VFAs) using dark fermentation (DF) is a promising technique for developing a sustainable ecosystem. However, poor performance of DF caused by vulnerable fermentative bacteria vitality and strong LL toxicity significantly hinder its commercialization. Herein, an integrated technique linking microalgae photosynthesis and DF was proposed, in which mixed microalgae were applied to robustly reclaim nutrients and chemical oxygen demand (COD) from LL. Then, microalgae biomass was fermented into biohydrogen and VFAs using the DF process. Underlying synergistic mechanisms of the interaction of Scenedesmus obliquus and Chlorella vulgaris resulting from the functioning of extracellular polymeric substances (EPS) were discussed in detail. For better absorption of nutrients from LL, the mixed microalgae secreted obviously more EPS than pure microalgae, which played vital roles in the assimilation of cellular nutrients by forming more negative zeta potential and secreting more tyrosine-/tryptophan-family proteins in EPS. Besides, mixed microalgae produced more intracellular proteins and carbohydrates than the pure microalgae, thereby providing more feedstock for DF and achieving higher energy yield of 10.80 kJ/L than 6.64 kJ/L that was obtained when pure microalgae were used. Moreover, the energy conversion efficiency of 7.75% was higher for mixed microalgae than 4.77% that was obtained for pure microalgae. This work may inspire efficient disposal of LL and production of bioenergy, together with filling the knowledge gaps of synergistic mechanisms of dual microalgal interactions.

Improving reverse osmosis concentrate treatment and nutrients conversion to Chlorella vulgaris bioenergy assisted with granular activated carbon.

Landfill leachate (LL), especially the reverse osmosis concentrate (ROC), is a societal burden due to high toxicity but may have intrinsic values attributing to copious nutrients and organics. ROC bioremediation by microalgae has attracted much attentions benefiting from its extra advantage of bioenergy production. However, efficient microalgae cultivation with ROC is still a challenging task attributing to notorious ROC characteristics, like high chromaticity and toxicity. To alleviate these negative influences, a technique integrating granular activated carbon (GAC) pretreatment and microalgae bioremediation was proposed, with which nitrogen and phosphorus removal efficiencies achieved 100% along with an optimized microalgal biomass concentration of 1.44 g/L and lipid yield of 482.4 mg/L. Furthermore, a total volumetric energy yield of 33.6 kJ/L was acquired, which was conducive to realize energy valorization. The visualization evidence of three-dimensional fluorescence spectroscopy revealed chromaticity degradation mechanism of ROC as humic acids reduction and transfer to family of soluble microbial by-products. Meanwhile, contributions of GAC adsorption and microalgae assimilation on nutrients removal were analyzed. Together, this work provides a promising method and valuable information for ROC bioremediation with microalgae.

Automatic Quantification of Subsurface Defects by Analyzing Laser Ultrasonic Signals Using Convolutional Neural Networks and Wavelet Transform.

The conventional machine learning algorithm for analyzing ultrasonic signals to detect structural defects necessarily identifies and extracts either time- or frequency-domain features manually, which has problems in reliability and effectiveness. This work proposes a novel approach by combining convolutional neural networks (CNNs) and wavelet transform to analyze the laser-generated ultrasonic signals for detecting the width of subsurface defects accurately. The novelty of this work is to convert the laser ultrasonic signals into the scalograms (images) via wavelet transform, which are subsequently utilized as the image input for the pretrained CNN to extract the defect features automatically to quantify the width of defects, avoiding the necessity and inaccuracy induced by artificial feature selection. The experimentally validated numerical model that simulates the interaction of laser-generated ultrasonic waves with subsurface defects is first established, which is further utilized to generate adequate laser ultrasonic signals for training the CNN model. A total number of 3104 data are obtained from simulation and experiments, with 2480 simulated signals for training the CNN model and the remaining 620 simulated data together with 4 experimental signals for verifying the performance of the proposed algorithm. This approach achieves the prediction accuracy of 98.5% on validation set, particularly with the prediction accuracy of 100% for the four experimental data. This work proves the feasibility and reliability of the proposed method for quantifying the width of subsurface defects and can be further expanded as a universal approach to various other defects detection, such as defect locations and shapes.