The development of a generalized multilevel inverter for symmetrical and asymmetrical dc sources with a minimized ON state switch

This paper proposes a double source double diode double switch (DSDDDS) multilevel inverter to generate positive voltage and a connecting polarity changing circuit of an H-bridge inverter to generate negative voltage. By connecting the jth number of basic units in series, the desired level is obtained. Various algorithms, such as Natural, Binary, Trinary, and Quasi-Linear sequences, and two proposed algorithms are discussed to determine the magnitude of DC voltage sources in order to generate more stepped levels with fewer switches. The proposed multilevel inverter eliminates the need to turn on additional power switches for different levels, which is the main advantages of this topology. The proposed multilevel inverter is compared to conventional switched diode multilevel inverters in terms of switch count, number of ON state switches per level, driver circuits, and total standing voltage. Real-time results from the OPAL-RT test bench and simulation have validated the proposed inverter

Pathogenicity and proteome production of Isaria fumosorosea (=Paecilomyces Fumosoroseus) (WISE) isolates against lemon butterfly, Papilio demoleus (Papilionidae: lepidoptera)

The pathogenic potential and catalytic triad conserved amino acids of the isolates Isaria fumosorosea (=Paecilomyces fumosoroseus) (Ifr1 and Ifr2) in response to Papilio demoleus was analysed. The isolates showed its potential in killing P. demoleus causing mortality of 72.23 and 61.90% at the end of 8 days with 108 spores ml-1 concentrations. The enzyme assays (higher proteolytic and chitinolytic activity) also showed that the Ifr2 was more efficient than Ifr1. The predictions of catalytic triads (serine, histidine and asparagine) were also visualized in the peak level obtained in infra-red (IR) and H1 nuclear magnetic resonance (NMR) spectra. With this information it was suggested that, partial characterization of catalytic domain was predicted in the fungal isolates Ifr.Keywords:  Entomopathogenic fungi, Isaria fumosorosea, Papilio demoleus, biological control.African Journal of Biotechnology, Vol 13(43) 4176-418

A compute-in-memory chip based on resistive random-access memory.

Realizing increasingly complex artificial intelligence (AI) functionalities directly on edge devices calls for unprecedented energy efficiency of edge hardware. Compute-in-memory (CIM) based on resistive random-access memory (RRAM)1 promises to meet such demand by storing AI model weights in dense, analogue and non-volatile RRAM devices, and by performing AI computation directly within RRAM, thus eliminating power-hungry data movement between separate compute and memory2-5. Although recent studies have demonstrated in-memory matrix-vector multiplication on fully integrated RRAM-CIM hardware6-17, it remains a goal for a RRAM-CIM chip to simultaneously deliver high energy efficiency, versatility to support diverse models and software-comparable accuracy. Although efficiency, versatility and accuracy are all indispensable for broad adoption of the technology, the inter-related trade-offs among them cannot be addressed by isolated improvements on any single abstraction level of the design. Here, by co-optimizing across all hierarchies of the design from algorithms and architecture to circuits and devices, we present NeuRRAM-a RRAM-based CIM chip that simultaneously delivers versatility in reconfiguring CIM cores for diverse model architectures, energy efficiency that is two-times better than previous state-of-the-art RRAM-CIM chips across various computational bit-precisions, and inference accuracy comparable to software models quantized to four-bit weights across various AI tasks, including accuracy of 99.0 percent on MNIST18 and 85.7 percent on CIFAR-1019 image classification, 84.7-percent accuracy on Google speech command recognition20, and a 70-percent reduction in image-reconstruction error on a Bayesian image-recovery task