Smooth and safe stop: Fixed-time fault tolerant control for heavy legged robot with active identification on tolerance capability.

Heavy-legged robots (HLRs), integral to optimizing efficiency in manufacturing and transportation, rely on advanced active servo fault diagnosis and fault-tolerant control (FTC) mechanisms. This study presents an FTC framework with active fault status identification, fault tolerance capability assessment, and model uncertainty handling. A key contribution is the introduction of an active servo fault state estimator (ASFSE), which enables real-time monitoring of servo status by comparing residual differences between servo and controller outputs.

Predicting the DNA binding specificity of mutated transcription factors using family-level biophysically interpretable machine learning.

Sequence-specific interactions of transcription factors (TFs) with genomic DNA underlie many cellular processes. High-throughput binding assays coupled with computational analysis have made it possible to accurately define such sequence recognition in a biophysically interpretable yet mechanism-agonistic way for individual TFs. The fact that such sequence-to-affinity models are now available for hundreds of TFs provides new avenues for predicting how the DNA binding specificity of a TF changes when its protein sequence is mutated.

Servo torque fault diagnosis implementation for heavy-legged robots using insufficient information.

The reliability of sensors and servos is paramount in diagnosing the Heavy-Legged Robot (HLR). Servo faults stemming from mechanical wear, environmental disturbances, or electrical issues pose significant challenges to traditional diagnostic methods, which rely heavily on delicate sensors. This study introduces a framework that solely relies on joint position and permanent magnet synchronous motor (PMSM) information to mitigate dependency on fragile sensors for servo-fault diagnosis.

Novel Few-Shot Learning Neural Network for Predicting Carbohydrate-Active Enzyme Affinity Toward Fructo-Oligosaccharides.

The enzymatic activity of the microbiome toward carbohydrates in the human digestive system is of enormous health significance. Predicting how carbohydrates through food intake may affect the distribution and balance of gut microbiota remains a major challenge. Understanding the enzyme/substrate specificity relationship of the carbohydrate-active enzyme (CAZyme) encoded by the vast gut microbiome will be an important step to address this question.