HungerGist: An Interpretable Predictive Model for Food Insecurity

The escalating food insecurity in Africa, caused by factors such as war, climate change, and poverty, demonstrates the critical need for advanced early warning systems. Traditional methodologies, relying on expert-curated data encompassing climate, geography, and social disturbances, often fall short due to data limitations, hindering comprehensive analysis and potential discovery of new predictive factors. To address this, this paper introduces "HungerGist", a multi-task deep learning model utilizing news texts and NLP techniques. Using a corpus of over 53,000 news articles from nine African countries over four years, we demonstrate that our model, trained solely on news data, outperforms the baseline method trained on both traditional risk factors and human-curated keywords. In addition, our method has the ability to detect critical texts that contain interpretable signals known as "gists." Moreover, our examination of these gists indicates that this approach has the potential to reveal latent factors that would otherwise remain concealed in unstructured texts

High‐Yield‐Stress Particle‐Stabilized Emulsion for Form‐Factor‐Free Thermal Pastes with High Thermal Conductivity, Stability, and Recyclability

Abstract Thermal pastes, thermally conductive fillers dispersed in liquid matrices, are widely used as thermal interface materials (TIMs). TIMs transfer heat generated from electronics to the surroundings, ensuring optimal operating temperatures. Thus, it is crucial to obtain high thermal conductivity (TC) by forming a continuous heat‐conduction pathway through interconnected filler‐networks within the TIM. Therefore, for paste‐type TIMs with spherical fillers, high TC can only be realized at sufficiently high filler loadings (>60 vol%). However, the pastes bearing such high filler loadings are thick, stiff, and less applicable. To these ends, particle‐stabilized emulsions composed of immiscible liquids (silicone oil and glycerol) and spherical alumina are utilized as thermal pastes. Owing to this structure, the resulting form‐factor‐free thermal paste exhibits higher TC and stability than a simple mixture consisting of alumina and a single‐liquid‐matrix (either silicone oil or glycerol). Furthermore, the high applicability of the emulsion‐type pastes enables syringe extrusion, 3D printing, multiple cycles of reprocessing/molding, and eco‐friendly recycling