Selection of decoupling capacitors (decaps) is important for power distribution network (PDN) design in terms of lowering impedance and saving cost. Good PDN designs typically mean satisfying a target impedance with as less decaps as possible. In this paper, an inductance-based method is utilized to calculate the port priority fist, and afterwards deep reinforcement learning (DRL) with deep neural network (DNN) is applied to optimize the assignment of decaps on the prioritized locations. The DRL algorithm can explore by itself without any prior physical knowledge, and the DNN is trained with the exploration experience and eventually converges to an optimum state. The proposed hybrid method was tested on a printed-circuit-board (PCB) example. After some iterations of training the DNN successfully reached to an optimum design, which turned out to be the minimum number of decaps that can satisfy the target impedance. The usage of DRL with DNN makes the algorithm promising to include more variables as input and handle more complicated cases in the future.

• A broad-spectrum Salmonella Siphophage T102 was isolated and characterized. • Phage T102 possessed a short latent period, adequate stability, and large burst size. • Phage T102 exhibited strong lytic activity against multidrug-resistant Salmonella. • Phage T102 contained no genes implicated in virulence and drug resistance. • Phage T102 prevented biofilm formation and eliminated formed biofilms.

Staphylococcus aureus (S. aureus) is a major foodborne pathogen. Rapid and specific detection is crucial for controlling staphylococcal food poisoning. This study reported a Staphylococcus phage named LSA2302 showing great potential for applications in the rapid detection of S. aureus. Its biological characteristics were identified, including growth properties and stability under different pH and temperature conditions. The genomic analysis revealed that the phage has no genes associated with pathogenicity or drug resistance. Then, the phage-functionalized magnetic beads (pMB), serving as a biological recognition element, were integrated with ATP bioluminescence assays to establish a biosensing method for S. aureus detection. The pMB enrichment brought high specificity and a tenfold increase in analytical sensitivity during detection. The whole detection process could be completed within 30 min, with a broad linear range of 1 × 10⁴ to 1 × 10⁸ CFU/mL and a limit of detection (LOD) of 2.43 × 10³ CFU/mL. After a 2 h pre-cultivation, this method is capable of detecting bacteria as low as 1 CFU/mL. The recoveries of S. aureus in spiked skim milk and chicken samples were 81.07% to 99.17% and 86.98% to 104.62%, respectively. Our results indicated that phage-based biosensing can contribute to the detection of target pathogens in foods.