Two interface effects: Exchange bias and magnetic proximity

/pdf/climate-change-and-chronic-food-insecurity-in-sub-saharan-5e2raryf.pdf

Climate Change and Chronic Food Insecurity in Sub-Saharan Africa

Role of the antiferromagnetic bulk spins in exchange bias

By ascertaining NiO surface roughness in a Ni80Fe20/NiO film system, we were able to correlate the effects of altered interface roughness from low-energy ion-beam bombardment of the NiO layer and the different thermal instabilities in the NiO nanocrystallites. From experiment and by modelling the temperature dependence of the exchange bias field and coercivity, we have found that reducing the interface roughness and changing the interface texture from an irregular to striped conformation enhanced the exchange coupling strength. Our results were in good agreement with recent simulations using the domain state model that incorporated interface mixing.

Tailoring interfacial exchange coupling with low-energy ion beam bombardment: Tuning the interface roughness

Globalization and interconnected supply chains have led to complex disruptions in global value chains, caused by various factors such as natural disasters, climate events, geopolitical conflicts, and economic crises. Recent breakthroughs in AI, machine learning, blockchain, and big data analytics offer new possibilities for forecasting and managing these disruptions effectively. This study examines the role of AI in forecasting and managing disruptions within global value chain to tackle food insecurity. We conducted a bibliometric and scientometric analysis using comprehensive data from Scopus and Web of Science to explore emerging research trends, influential publications, leading institutions, collaborations, themes, policy implications, and future research avenues. The research revealed an average yearly growth rate of 13.78 % in publications from 1973 to 2022. China, the United Kingdom, and the United States lead in AI applications to address supply chain disruptions, particularly concerning food insecurity. Frequently used keywords include "food security," "supply chain management," "agriculture," "modelling," "climate change," and "COVID-19." Themes identified focus on the impact of COVID-19 on food supply chains, achieving food security amidst climate change, leveraging predictive models in agriculture, and assessing the impact of disruptions on food price volatility and global supply chain risk assessment approaches. The insights gained from this research offer valuable guidance for policymakers and researchers to enhance food security. The identified themes provide direction for future research efforts in advancing food security amidst uncertainties and disruptions in global value chains. 

Forecasting disruptions in global food value chains to tackle food insecurity: The role of AI and big data analytics – A bibliometric and scientometric analysis

The influence of an imperfect interface on exchange bias (EB) properties is investigated. Within the framework of the domain state model, the EB field HEB and the coercive field HC are determined using computer simulations, and they are found to depend strongly on the details of the interface structure. This dependence is sensitive to the dilution of the antiferromagnet (AFM) with non-magnetic defects in the bulk. For the optimal interface structure, giving greatest EB, the optimal dilution is found to be much less than that for an ideal-interface system, taking a value in better agreement with experimental results. Even without any defects in the bulk of the AFM the interface roughness leads to EB for thin antiferromagnetic layers, in accordance with the model by Malozemoff. Finally, the thickness dependence of rough-interface systems is found to differ significantly from that of ideal-interface systems.

/pdf/exchange-bias-in-ferromagnetic-antiferromagnetic-bilayers-30oiu1zplc.pdf

Exchange bias in ferromagnetic/antiferromagnetic bilayers with imperfect interfaces

This paper analyzes the domestic and external drivers of local staple food prices in Sub-Saharan Africa. Using data on domestic market prices of the five most consumed staple foods from 15 countries, this paper finds that external factors drive food price inflation, but domestic factors can mitigate these vulnerabilities. On the external side, our estimations show that Sub-Saharan African countries are highly vulnerable to global food prices, with the pass-through from global to local food prices estimated close to unity for highly imported staples. On the domestic side, staple food price inflation is lower in countries with greater local production and among products with lower consumption shares. Additionally, adverse shocks such as natural disasters and wars bring 1.8 and 4 percent staple food price surges respectively beyond generalized price increases. Economic policy can lower food price inflation, as the strength of monetary policy and fiscal frameworks, the overall economic environment, and transport constraints in geographically challenged areas account for substantial cross-country differences in staple food prices. RECOMMENDED CITATION: Okou, C., Spray, J., and Unsal, D. F. 2022. Staple Food Prices in Sub-Saharan Africa: An Empirical Assessment, IMF Working Papers, WP/22/135. JEL Classification Numbers: E31; O11; O55; Q02; Q18

Staple Food Prices in Sub-Saharan Africa: An Empirical Assessment

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Climate Change and Chronic Food Insecurity in Sub-Saharan Africa

Exchange bias in ferromagnetic/antiferromagnetic bilayers with imperfect interfaces

Staple Food Prices in Sub-Saharan Africa: An Empirical Assessment