Scientific Advances in the Last Decade on the Recovery, Characterization, and Functionality of Bioactive Compounds from the Araticum Fruit (Annona crassiflora Mart.)

Araticum (Annona crassiflora Mart.) is a native and endemic species to Brazilian Cerrado whose fruits have high sensorial, nutritional, bioactive, and economic potential. Its use in local folk medicine, associated with recent scientific findings, has attracted growing interest from different industrial sectors. Therefore, understanding the scientific advances achieved so far and identifying gaps to be filled is essential to direct future studies and transform accumulated knowledge into innovative technologies and products. In this review, we summarize the phytochemical composition, bioactivities, and food products from araticum fruit that have been reported in the scientific literature over the past 10 years. The compiled data showed that araticum fruit parts contain a wide range of bioactive compounds, particularly phenolic compounds, alkaloids, annonaceous acetogenins, carotenoids, phytosterols, and tocols. These phytochemicals contribute to different biological activities verified in araticum fruit extracts/fractions, including antioxidant, anti-inflammatory, anti-Alzheimer, anticancer, antidiabetic, anti-obesity, antidyslipidemic, antinociceptive, hepatoprotective, healing of the cutaneous wound, antibacterial, and insecticide effects. Despite the promising findings, further studies-particularly toxicological (especially, with byproducts), pre-clinical, and clinical trials-must be conducted to confirm these biological effects in humans and assure the safety and well-being of consumers.

Risk-based merging decisions for autonomous vehicles

INTRODUCTION

The safe freeway merging operation for fully Autonomous Vehicles (AVs) in mixed traffic (i.e., the presence of AVs and non-AVs in a traffic stream) is a challenging task. Under a mixed traffic environment, an AV merging operation could significantly increase conflict risks and reduce operational efficiency.

METHOD

This study quantifies the freeway merging conflict risk and develops a freeway merging decision strategy based on conflict risk assessment for an AV attempting to merge to a traffic stream with non-AVs on the freeway. The performance of the risk-based merging decision strategy is evaluated in uncongested, near-congested, and congested traffic conditions.

RESULTS

The analyses show that the risk-based merging strategy causes less abrupt deceleration of an AV's immediate upstream vehicle in the target lane on the freeway compared to the based models (i.e., two models based on gap acceptance concepts and a safe gap model based on a surrogate measure, 'Time-to-Collision (TTC)'). The risk-based merging strategy meets the minimum safe gap between an AV intending to merge and its immediate downstream vehicle in the target lane. The risk-based merging strategy produces lower conflict risk in terms of 'Time Exposed Time-to-Collision (TET)' and 'Time Integrated Time-to-Collision (TIT)' compared to the base models. Moreover, the risk-based merging strategy has a lower impact on the average speed of traffic in the target lane compared to the base models considered in this study.

CONCLUSIONS

The risk-based merging strategy shows higher safety benefits for an AV's merging operation compared to base models.

PRACTICAL APPLICATIONS

The findings of this research would help design AV controllers for improving the safety of an AV merging operation in a mixed traffic stream.

Application of Neural Network Model Based on Multispecies Evolutionary Genetic Algorithm to Planning and Design of Diverse Plant Landscape

In order to explore the feasibility of applying neural network model to landscape planning, based on the multispecies evolutionary genetic algorithm, a neural network model is proposed in this paper for the system design of diverse plant landscape planning. From the perspective of plant species diversity, this paper discusses landscape planning based on a neural network model. This landscape plan involves more than 180 plant species, mainly shrubs, fungi, and so on. The application of multispecies evolutionary genetic algorithm to landscape planning and design and the application of gene level coding and multispecies parallel evolution strategy to the evolutionary design of neural network have guiding significance for plant landscape planning and design. Compared with the traditional neural network modeling method and genetic algorithm, the proposed method has the advantages of wide network structure search space and simple algorithm calculation and design, independent of specific application background, and has strong application and promotion value. This method makes the model performance evaluation index more comprehensive and accurate and the model solution more reasonable. At the same time, combined with the specific status and corresponding changes of various plants in each season, this paper designs a targeted plan to rationally plan the specific spatial layout of the plant landscape and the combination of different types of plant landscapes, so as to effectively improve the quality of the landscape.

Genomic, Habitat, and Leaf Shape Analyses Reveal a Possible Cryptic Species and Vulnerability to Climate Change in a Threatened Daisy

Olearia pannosa is a plant species listed as vulnerable in Australia. Two subspecies are currently recognised (O. pannosa subsp. pannosa (silver daisy) and O. pannosa subsp. cardiophylla (velvet daisy)), which have overlapping ranges but distinct leaf shape. Remnant populations face threats from habitat fragmentation and climate change. We analysed range-wide genomic data and leaf shape variation to assess population diversity and divergence and to inform conservation management strategies. We detected three distinct genetic groupings and a likely cryptic species. Samples identified as O. pannosa subsp. cardiophylla from the Flinders Ranges in South Australia were genetically distinct from all other samples and likely form a separate, range-restricted species. Remaining samples formed two genetic clusters, which aligned with leaf shape differences but not fully with current subspecies classifications. Levels of genetic diversity and inbreeding differed between the three genetic groups, suggesting each requires a separate management strategy. Additionally, we tested for associations between genetic and environmental variation and carried out habitat suitability modelling for O. pannosa subsp. pannosa populations. We found mean annual maximum temperature explained a significant proportion of genomic variance. Habitat suitability modelling identified mean summer maximum temperature, precipitation seasonality and mean annual rainfall as constraints on the distribution of O. pannosa subsp. pannosa, highlighting increasing aridity as a threat for populations located near suitability thresholds. Our results suggest maximum temperature is an important agent of selection on O. pannosa subsp. pannosa and should be considered in conservation strategies. We recommend taxonomic revision of O. pannosa and provide conservation management recommendations.