Mycotoxin contamination in wheat is strongly influenced by weather conditions, yet how contamination may evolve under future climate and socioeconomic change remains poorly understood at the European scale. Here, we develop a hybrid modelling framewo... read more
Spintronic nano-neurons offer a promising route towards energy-efficient, high performance hardware neural networks thanks to their inherent low-input nonlinear dynamics. However, training such networks remains a major bottleneck as it depends on ove... read more
Computer-assisted surgery research requires large, deeply annotated video datasets that capture clinical and technical variability. Existing cataract surgery resources lack the diversity and annotation depth required to train generalizable deep-learn... read more
Accurate and reliable crop yield prediction before harvest is essential for informed decision-making, food security, resource optimization, climate change mitigation, and smart agricultural systems. Deep learning has significantly improved accuracy i... read more
This paper presents EcoImpact, a novel interpretable predictive-control framework for building energy management that integrates data-driven forecasting, iterative control optimization, and explainable artificial intelligence in a unified workflow. U... read more
OBJECTIVES: To evaluate three-dimensional anterior tooth position changes during orthodontic retention and to compare the effectiveness of three distinct fixed retainer fabrication designs including robotically bent retainers alongside with removable... read more
Genomic selection (GS) uses genome-wide molecular markers and phenotypic data from a training population to predict breeding values or phenotypes in candidate populations. Unlike marker-assisted selection, GS does not require significance testing of ... read more
Diuretic resistance represents a major source of heterogeneity in loop diuretic response and remains a key barrier to effective decongestion in heart failure. A key clinical challenge is the early identification of patients at high risk of an inadequ... read more
Laparoscopic cholecystectomy is a high-volume procedure with relatively short operative times, leaving limited margin for further reduction in mean duration. However, variability in operative performance, compounded by anatomical variation and differ... read more
Artificial intelligence has emerged as a promising approach for improving the detection and management of intraoperative bleeding during conventional and robotic-assisted laparoscopic surgery, where delayed recognition of hemorrhage can lead to incre... read more
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