An agent based model representation to assess resilience and efficiency of food supply chains

Systems thinking and modelling to support transformative change: key lessons from inter-disciplinary analysis of socio-ecological systems in applied land systems research

The evolving 'permacrisis' of compounding environmental and social challenges calls for transformative approaches to understanding and intervening in socio-ecological systems. Approaches to support systems thinking and understanding can be vital to achieving this goal. However, applying such systems thinking is often challenging, and we need to better reflect on the pros and cons of different approaches for building systems understanding and informing changes. In this paper, we first identify key attributes of systems thinking approaches from literature. We then use these as a framework for comparing and evaluating seven different systems thinking approaches, selected on the basis of our experience in applying them in support of the management and governance of various types of land systems. The seven approaches are: agent-based modelling, Bayesian belief networks, causal loop modelling, spatial multicriteria analysis, societal metabolic analyses, social network mapping and quantitative story telling. This framework has allowed us to appraise and reflect on our own experiences to identify the respective strengths and weaknesses of these different methodologies. We note that some of the ability to inform change depends as much on the context within which specific tools are used as the particular features of the tools themselves. Based on our appraisal, we conclude by suggesting six key recommendations that should be followed by others seeking to commission and use systems approaches, in order to enable them to support transformative change. We hope this may be useful to those working with systems approaches, since there is an urgent need for analytic efforts that can inform and enable transformative change. We also reiterate the call for sustained funding for long-term, standards-based evaluation of systems thinking approaches with respect to whether their use can demonstrate instrumental impacts leading to the kind of transformation the IPCC has called for, i.e. fundamental system change that goes beyond capacity development impacts such as network-building.

Compassionate Care with Autonomous AI Humanoid Robots in Future Healthcare Delivery: A Multisensory Simulation of Next-Generation Models

The integration of AI and robotics in healthcare raises concerns, and additional issues regarding autonomous systems are anticipated. Effective communication is crucial for robots to be seen as "caring", necessitating advanced mechatronic design and natural language processing (NLP). This paper examines the potential of humanoid robots to autonomously replicate compassionate care. The study employs computational simulations using mathematical and agent-based modeling to analyze human-robot interactions (HRIs) surpassing Tetsuya Tanioka's TRETON. It incorporates stochastic elements (through neuromorphic computing) and quantum-inspired concepts (through the lens of Martha Rogers' theory), running simulations over 100 iterations to analyze complex behaviors. Multisensory simulations (visual and audio) demonstrate the significance of "dynamic communication", (relational) "entanglement", and (healthcare system and robot's function) "superpositioning" in HRIs. Quantum and neuromorphic computing may enable humanoid robots to empathetically respond to human emotions, based on Jean Watson's ten caritas processes for creating transpersonal states. Autonomous AI humanoid robots will redefine the norms of "caring". Establishing "pluralistic agreements" through open discussions among stakeholders worldwide is necessary to align innovations with the values of compassionate care within a "posthumanist" framework, where the compassionate care provided by Level 4 robots meets human expectations. Achieving compassionate care with autonomous AI humanoid robots involves translating nursing, communication, computer science, and engineering concepts into robotic care representations while considering ethical discourses through collaborative efforts. Nurses should lead the design and implementation of AI and robots guided by "technological knowing" in Rozzano Locsin's TCCN theory.

Key determinants to supply chain resilience to face pandemic disruption: An interpretive triple helix framework

Today, supply chain (SC) networks are facing more disruptions compared to the past. While disruptions are rare, they can have catastrophic long-term economic or societal repercussions, and the recovery processes can be lengthy. These can tremendously affect the SC and make it vulnerable, as observed during the COVID-19 pandemic. The identification of these concerns has prompted the demand for improved disruption management by developing resilient, agile, and adaptive SC. The aim of this study is to introduce an assessment framework for prioritizing and evaluating the determinants to supply chain resilience (SCR). To analyze the empirical data, fuzzy criteria importance through intercriteria correlation (fuzzy CRITIC) and fuzzy technique for order of preference by similarity to ideal solution (fuzzy TOPSIS) have been incorporated. Fuzzy CRITIC method was used to identify the critical determinants and fuzzy TOPSIS method was applied for determining relative ranking of some real-world companies. Finally, by developing propositions an interpretive triple helix framework was proposed to achieve SCR. This research stands out for its originality in both methodology and implications. By introducing the novel combination of Fuzzy CRITIC and Fuzzy TOPSIS in the assessment of determinants to SCR and applying these determinants with the help of interpretive triple helix framework to establish a resilient SC, this study offers a unique and valuable contribution to the field of SCR. The key findings suggest that 'Responsiveness' followed by 'Managerial coordination and information integration' are the most significant determinant to achieve SCR. The outcome of this work can assist the managers to achieve SCR with improved agility and adaptivity.

Systematic review on ensuring the global food security and covid-19 pandemic resilient food systems: towards accomplishing sustainable development goals targets

Covid-19, one of the most critical and widespread global pandemics, has resulted in extraordinary risk corollaries engulfing millions of people's lives and has caused an unprecedented economic downturn while amplifying food insecurity. A systematic review of 132 scientific communications was performed over a 15-year period, using articles from the ScienceDirect and Web of Science databases (2006-2021). In addition, 24 policy briefs, country papers, and publications from the UN, WHO, FAO, and OECD were cited. The aim of this paper is to provide a comprehensive review of existing literature on the adverse effects of the Covid-19 pandemic on agricultural food systems, as well as potential strategies for building robust, resilient, and sustainable food systems to ensure global food security, safety, and endeavors regarding future global emergencies, as well as new research policies while achieving SDG targets. This would fill a research gap while also having long-term implications for health, agricultural, and food resilience policy development in a rapidly changing world. Covid-19 demonstrates how human, animal, and environmental health are all interconnected, emphasizing the need for one health legislation and a paradigm shift in planetary health. Furthermore, it identifies potential mechanisms for rebuilding better systems by shifting priorities toward policy coherence, innovative food system governance, re-engineering market access, and nexus thinking in the food system approach. According to our findings, the COVID-19 posed unavoidable impediments to achieving SDG targets for food security and household poverty.

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Plate Waste Forecasting Using the Monte Carlo Method for Effective Decision Making in Latvian Schools

Food waste is a global problem, which becomes apparent at various stages of the food supply chain. The present research study focuses on the optimization of food consumption in schools and effective food management through data-driven decision making within the trends: zero food waste and digital transformation. The paper presents a plate waste forecasting system based on mathematical modeling and simulation using the Monte Carlo method, which showed an RMSE equal to ±3% and a MAPE of 10.15%. The solution based on the simulator provides a possibility to better understand the relationship between the parameters investigated through data visualization and apply this knowledge to train managers to make decisions that are more effective. The developed system has multi-disciplinary application: forecasting, education and decision making targeted to reduce food waste and improve public health and food management in schools.