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This paper reflects on the outcomes of a student-centered workshop held during the Indian Ecological Society International Conference, held at Punjab Agricultural University, Ludhiana, Punjab, 12 – 15 November 2024.The students' workshop was a follow-up on the topics developed in the Symposium on “Agriculture, Climate Changes, and Energy Transitions: Building Capacity to Maintain Yields of Innovative Agrifood Systems” held during the conference. Its major purpose was to encourage discussions among conference participants about specific research agendas and potential teaching plans. Students worked in groups of four to five to provide ideas for improving teaching and learning and ideas for future research. They consistently mentioned a need to grasp the practical application of their classroom studies. Most of the research ideas focused on conservation or enhancement of resources used in agriculture and many involved cross-disciplinary topics, such as the expansion of data analysis technologies to promote resilience for agricultural communities. The results parallel the principles outlined as key to a bioeconomy, outlined by the Food and Agriculture Organization of the United Nations. 

This is a preprint. It has not been peer reviewed yet. 

Noise typically degrades the performance of physical systems. In some cases, however, noise can provide an enhanced effect. For instance, stochastic resonance may be observed in a nonlinear oscillator when a weak signal is boosted by noise. On the other hand, adaptive oscillators are a type of nonlinear oscillator that can learn and store information in dynamic states. Here, noise is shown to increase the learning rate of an adaptive oscillator, using a Duffing adaptive oscillator. To highlight this effect, stochastic simulations are employed, and the Fokker–Planck equation is semi-analytically solved using the cumulant neglect method. As adaptive oscillators can also be used as a powerful physical reservoir computer architecture, this work shows that the Duffing adaptive oscillator can be optimized for fast learning in noisy environments. 

Climate change agreements force us to explore interconnected social, economic, and energy related transitions needed to combat the effects of anthropogenic activities on the Earth's climate. To help us understand what brought us to where we are today and guide us in our decision-making for tomorrow, we introduce the Energy Regulatory Industrial Complex model (ERIC). ERIC illustrates the transformation of energy from energy sources through to energy services, as in energy flow diagrams, but adds the emissions greenhouse gases from those transformations. ERIC includes the creation of wealth enabled by energy services, innovation that supports advances in science and technology, and activities that maintain and sustain existing systems. Policies and geo-politics can influence each decision and investment stream. At its core, ERIC relies on a firm foundation of education. Using data from the United States and India, we show how ERIC can be used to pinpoint the vulnerabilities and places for interventions in transitioning to a clean energy future.