Pig Face Open Set Recognition and Registration Using a Decoupled Detection System and Dual-Loss Vision Transformer.

Effective pig farming relies on precise and adaptable animal identification methods, particularly in dynamic environments where new pigs are regularly added to the herd. However, pig face recognition is challenging due to high individual similarity, lighting variations, and occlusions. These factors hinder accurate identification and monitoring.

Optimized System Identification (SI) of Brushless DC (BLDC) motor using Data-Driven Modeling Methods.

This study investigates the nonlinear dynamics of Brushless DC (BLDC) motors using the MATLAB/Simulink platform, emphasizing system identification through the Least Squares (LS) method and Nonlinear Autoregressive Network With Exogenous Inputs (NARX) models with variable regressors. Accurate data-driven models derived from these techniques are essential for designing efficient feedback control systems, enabling precise motor dynamics representation and facilitating early fault detection by identifying deviations from normal operation. A detailed simulation of the BLDC motor under no-load conditions is performed to analyze the speed response and ripple effects in torque and speed, underscoring the need for effective modeling and control.

Bio-preservative potential of marjoram and fennel essential oil nano-emulsions against toxigenic fungi in citrus: integrating , -, and - approaches.

Citrus fruits, known for their vibrant flavours and health benefits, are susceptible to fungal attacks, particularly from toxigenic fungi, which pose a significant pre- and post-harvest hazard. However, aromatic oils and their nanoparticles may effectively address this issue. Marjoram and fennel oils, alongside their nanoparticles, were extracted, and their aromatic constituents and antimicrobial activities were evaluated.

Artificial intelligence versus radiologists in detecting early-stage breast cancer from mammograms: a meta-analysis of paradigm shifts.

Purpose: Early detection of breast cancer is crucial for improving patient outcomes. With advancements in artificial intelligence (AI), there is growing interest in its potential to assist radiologists in interpreting mammograms for early cancer detection. AI algorithms offer the promise of increased accuracy and efficiency in identifying subtle signs of breast cancer, potentially complementing the expertise of radiologists and enhancing the screening process for early-stage breast cancer detection.