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The global food industry generates substantial waste, posing significant environmental, economic, and social challenges. This dissertation explores circular business strategies for food waste management, aiming to develop an efficient model that integrates circular economy principles and innovative technologies. Key research questions include: What are current food waste management practices? How can circular economy principles reduce food waste effectively? What role can technology play in improving these systems? The study also examines barriers to implementation and identifies gaps in existing literature. The methodology involves a comprehensive literature review, case studies, and the development of a detailed mathematical model. The literature review covers circular economy concepts, current food waste treatment technologies, machine learning and Al applications in waste management. Case studies from various countries provide insights into regulatory frameworks and innovative solutions. Central to this research is the mathematical modelling of food waste management systems. The model employs Hamiltonian and/or Lagrangian formulations to optimise waste transportation and processing. This approach allows for the simulation of various scenarios, helping to identify the most efficient pathways for food waste reduction and resource recovery. The model also incorporates phase transitions better to understand the dynamics of waste generation and treatment processes. Phase transitions mark changes on tendencies and in this case they help us to evaluate the viability of the construction of a fast track for the transportation of food waste in any city. Results indicate that adopting circular economy principles in food waste management is feasible and beneficial. Effective strategies include bioplastics, insectutilisation, and machine learning models for waste prediction and management. The developed mathematical model suggests efficient waste transportation through a coupled network approach, ensuring rapid and effective waste evacuation. The research highlights the importance of technological integration and cross-sector collaboration for sustainable food waste management. It also stresses the need for robust regulatory frameworks and consumer education to drive behavioural changes and support circular practices

The spontaneous symmetry breaking phenomena applied to Quantum Finance considers that the martingale state in the stock market corresponds to a ground (vacuum) state if we express the financial equations in the Hamiltonian form. The original analysis for this phenomena completely ignores the kinetic terms in the neighborhood of the minimal of the potential terms. This is correct in most of the cases. However, when we deal with the martingale condition, it comes out that the kinetic terms can also behave as potential terms and then reproduce a shift on the effective location of the vacuum (martingale). In this paper, we analyze the effective symmetry breaking patterns and the connected vacuum degeneracy for these special circumstances. Within the same scenario, we analyze the connection between the flow of information and the multiplicity of martingale states, providing in this way powerful tools for analyzing the dynamic of the stock markets.

We are delighted to present this special issue editorial for Neural Computing and Applications special issue on LatinX in AI research. This special issue brings together a collection of articles that explore machine learning and artificial intelligence research from various perspectives, aiming to provide a comprehensive and in-depth understanding of what LatinX researchers are working on in the field. In this editorial, we will introduce the overarching theme of the special issue, highlight the significance of the selected papers, and offer insights into the contributions made by the authors. The LatinX in AI organization was launched in 2018, with leaders from organizations in Artificial Intelligence, Education, Research, Engineering, and Social Impact with a purpose to together create a group that would be focused on “Creating Opportunity for LatinX in AI.” The main goal is to increase the representation of LatinX professionals in the AI industry. LatinX in AI Org and programs are volunteer-run and fiscally sponsored by the Accel AI Institute, 501(c)3 Non-Profit.

It is known that the probability is not a conserved quantity in the stock market, given the fact that it corresponds to an open system. In this paper we analyze the flow of probability in this system by expressing the ideal Black-Scholes equation in the Hamiltonian form. We then analyze how the non-conservation of probability affects the stability of the prices of the Stocks. Finally, we find the conditions under which the probability might be conserved in the market, challenging in this way the non-Hermitian nature of the Black-Scholes Hamiltonian.

In this chapter, a mathematical model explaining generically the propagation of a pandemic is proposed, helping in this way to identify the fundamental parameters related to the outbreak in general. Three free parameters for the pandemic are identified, which can be finally reduced to only two independent parameters. The model is inspired in the concept of spontaneous symmetry breaking, used normally in quantum field theory, and it provides the possibility of analyzing the complex data of the pandemic in a compact way. Data from 12 different countries are considered and the results presented. The application of nonlinear quantum physics equations to model epidemiologic time series is an innovative and promising approach.

At the beginning of 2020, the World Health Organization (WHO) started a coordinated global effort to counterattack the potential exponential spread of the SARS-Cov2 virus, responsible for the coronavirus disease, officially named COVID-19. This comprehensive initiative included a research roadmap published in March 2020, including nine dimensions, from epidemiological research to diagnostic tools and vaccine development. With an unprecedented case, the areas of study related to the pandemic received funds and strong attention from different research communities (universities, government, industry, etc.), resulting in an exponential increase in the number of publications and results achieved in such a small window of time. Outstanding research cooperation projects were implemented during the outbreak, and innovative technologies were developed and improved significantly. Clinical and laboratory processes were improved, while managerial personnel were supported by a countless number of models and computational tools for the decision-making process. This chapter aims to introduce an overview of this favorable scenario and highlight a necessary discussion about ethical issues in research related to the COVID-19 and the challenge of low-quality research, focusing only on the publication of techniques and approaches with limited scientific evidence or even practical application. A legacy of lessons learned from this unique period of human history should influence and guide the scientific and industrial communities for the future.