MicroVision (United States)

Antimicrobial resistance (AMR) is the ability of microbes to survive exposure to antimicrobials that were once effective against them. This is a major global crisis and public health challenge of the 21st century that is declared as a silent pandemic, with India among other countries considered one of the hotspots [R. Laxminarayan, A. Duse, C. Wattal, A. K. M. Zaidi, et al., Lancet Infect Dis 13:1057-1098, 2013, https://doi.org/10.1016/S1473-3099(13)70318-9]. The evolution of AMR in modern India's context is embedded in a complex interplay of the timing of antibiotic introduction, a growing dense population, cultural inheritances, infectious disease burden, developing health systems, evolving drug market regulations, environmental and animal sector drivers, socioeconomic factors, and policy/implementation gaps. Central to this discussion are the human patient, animal, and environmental aspects of antibiotic overuse from the historical aspects of AMR in India broadly tracing from early antibiotic use to the current "One Health" challenge. It highlights cultural factors, the evolution of surveillance systems, policy responses, India's efforts, and key lessons in antimicrobial stewardship.

Neural Additive Models (NAMs) have recently demonstrated promising predictive performance while maintaining interpretability. However, their capacity is limited to capturing only first-order feature interactions, which restricts their effectiveness on real-world datasets. To address this limitation, we propose Higher-order Neural Additive Models (HONAMs), an interpretable machine learning model that effectively and efficiently captures feature interactions of arbitrary orders. HONAMs improve predictive accuracy without compromising interpretability, an essential requirement in high-stakes applications. This advantage of HONAM can help analyze and extract high-order interactions present in datasets. The source code for HONAM is publicly available at https://github.com/gim4855744/HONAM/.

Rare-earth free biaxial excited synchronous machines (BESMs) are well-suited for achieving unity power factor (UPF) at base speed, enabling high power density and costeffective solutions in traction motor drive systems. With dual excitation sources, the rotor field current can be effectively controlled to optimize machine performance across the entire operating range of the motor drive system. This paper proposes a novel algorithm for the generation of optimal stator and rotor current commands, aimed at minimizing copper losses while ensuring UPF operation. The algorithm also guarantees compliance with stator and rotor voltage constraints under all operating conditions. Developed using the machine's analytical model and numerical optimization techniques, the proposed method is validated through comprehensive simulations across a wide range of torque and speed scenarios. The results confirm the algorithm's effectiveness in maintaining UPF and reducing copper losses, providing valuable insights for enhancing BESM efficiency in traction applications.

Non-ideal position estimation results in degraded performance of synchronous motor drive systems due to reduction of the average capability of the drive as well as torque harmonics of different orders. The signature and extent of the performance degradation is further dependent, quite significantly, on the current control architecture, i.e., feedforward or feedback control, employed. This paper presents a comprehensive analysis of non-idealities or errors in position estimation and their effects on the control performance of synchronous motor drives. Analytical models capturing the error in various signals caused by position sensing errors in the drive system for different control architectures are presented and are validated with simulation and experimental results on a prototype permanent magnet synchronous motor drive.

Mask cleaning can be one of the most troublesome steps in the mask making process. Automatic defect inspection and post pellicle inspection have established the requirement that a mask must emerge from a cleaning process virtually free of any films and particulate materials. A spin/spray cleaning system has been developed using conventional H₂SO₄ + H₂O₂ chemistry and hot water to clean and strip masks. The system was beta site tested at Micro Mask. The machine is described and preliminary test results are presented establishing the suitability of the spin/spray processor for most mask cleaning and stripping applications.