Combined drought and heat (DH) stress cause significant yield loss on crops such as edamame (Glycine max L. Merril) by disrupting the biochemical and physiological processes. The study aimed to investigate the effects of DH stress on the morphology and yield in three edamame cultivars (AGS429, UVE14, and UVE17) and to determine their associations with the biochemical and physiological responses. Yield indices were measured manually at full maturity. A laboratory scale was used to measure total seed mass per plant (TSMP) and 100 seed mass (100 SM). A leaf area meter was used to measure the leaf (LA). Correlations and principal component analysis (PCA) were determined using Genstat. The poor rate of photosynthesis, membrane damage and reduced cellulose synthesis (resulting in high numbers of flat and shattered pods) led to the highest yield reduction and poor seed quality in UVE17, contributing to its susceptibility to DH stress. The PCAs showed that LA and yield indices [total pods per plant (TPP), total seeds per plant (TSP), TSMP, and 100 SM] were strongly positively associated with AGS429. In contrast, UVE14 was associated with high carbohydrate accumulation (sucrose, trehalose, cellulose, and hemicellulose) at both growth stages. The higher rate of photosynthesis, antioxidant response systems, stable plasma membranes and cell wall re-enforcements in AGS429 and UVE14 contributed to yield enhancement and tolerance to DH stress. Therefore, AGS429 and UVE14 can be suggested for breeding programs due to moderate yield reduction (AGS429) and low yield reduction (UVE14) under DH stress.

Abstract
During the recent pandemic, the world has grown more sensitized and concerned about climate change as expressed by the outcomes of the recent COP26 and COP27 conferences in 2021 and 2022, respectively. The mission of the many participant nations was to reduce their dependence on fossil fuels in the generation of electricity, or to embrace a decarbonization process. That is, most nations are committed to rely more and more on clean energy sources such as solar, wind, hydroelectric, geothermic and, of course, nuclear. At this moment the USA still has the world largest fleet of light water reactors (92) which currently produce approximately 50% of the clean electricity consumed in the country. Recent legislation in the USA has provided funds to the private sector to prevent the premature shutdown of operating commercial nuclear power plants. The goal of the US Department of Energy is not only to keep existing nuclear plants operating but also to build new capacity through advanced reactors including micro reactors, small modular reactors, and larger scale Gen III and Gen IV reactors. It is argued that the commercialization of accident tolerant fuels (ATF) in the next quinquennium may contribute to the life extension of current light water reactors.

Biography
Dr. Raul B. Rebak is a corrosion research scientist at General Electric Research in Schenectady, New York since 2007. Previously he was the lead for materials corrosion testing in the Yucca Mountain Project at the University of California Lawrence Livermore National Laboratory. Raul has more than 30 years’ experience in environmental degradation processes of materials in many fields including nuclear power, oil & gas, energy storage, healthcare, transportation, and aerospace. Raul is a Fellow of both NACE International and ASM International.

Abstract
The citrus industry is Australia’s largest fresh fruit exporting industry by volume, with major export markets in Asian countries and the United States. However, the export of fresh produce (particularly fruit) is limited by quarantine restrictions in several countries. One of the major causes of these restrictions includes postharvest decay and defects. These post-harvest losses can reach 30% of the total production in developed
and 50% in less developed countries. National breakdown of on-farm losses for citrus is between 20-30% that equals 30-35,000 tones in Australia. We propose a novel, safe and low-cost citrus defect discrimination and grading mobile application based on computer vision and image analysis technique to sort healthy from defective and also, predict the early disease infestation that may result in reducing the application of commonly used synthetic fungicides (hence reduced MRLs) during post-harvest management. Deep neural networks (DNN) was employed to extract desired features with an appropriate set of filters applied on the given set of images. The system was able to automatically categorize and then sort the given various samples in respective classes in real-time citrus post-harvesting procedures. It is efficient, cost-effective, accurate and non-destructive, furthermore, it will improve inspection time and quality sorting guarantee in citrus production and supply chain management.Besides this the solution will allow farmers to reduce waste and achieve a sustainable and efficient gradingprocess, providing real-time information which accurately identifies fruit diseases and imperfections that
would also improve the efficiency of supply chain by 30-35%.

Biography
Professor Harvinder Singh has been an academic for nearly two decades with strong teaching and research expertise in applied informatics, traditional and molecular plant breeding approaches for the improvement and management of cereal crops, and medicinal plants. He has IPs for novel inventions in agriculture and has published over 50 research articles in peer reviewed journals, books and conference proceedings. He has also presented his research at national and international conferences.