Comparison between P&O and SSO techniques based MPPT algorithm for photovoltaic systems

Solar photovoltaic (SPV) systems are a renewable source of energy that are environmentally friendly and recyclable nature. When the solar panel is connected directly to the load, the power delivered to the load is not the optimal power. It is therefore important to obtain maximum power from SPV systems for enhancing efficiency. Various maximum power point tracking (MPPT) techniques of SPV systems were proposed. Traditional MPPT techniques are commonly limited to uniform weather conditions. This paper presents a study of MPPT for photovoltaic (PV) systems. The study includes a discussion of different MPPT techniques and performs comparison for the performance of the two MPPT techniques, the P&O algorithm, and salp swarm optimization (SSO) algorithm. MATLAB simulations are performed under step changes in irradiation. The results of SSO show that the search time of maximum power point (MPP) is significantly decreased and the MPP is obtained in the shortest time with high accuracy and minimum oscillations in the generated power when compared with P&O.

Integrative analysis of relative abundance data and presence-absence data of the microbiome using the LDM

We previously developed LDM for testing hypotheses about the microbiome that performs the test at both the community level and the individual taxon level. LDM can be applied to relative abundance data and presence-absence data separately, which work well when associated taxa are abundant and rare, respectively. Here we propose an omnibus test based on LDM that allows simultaneous consideration of data at different scales, thus offering optimal power across scenarios with different association mechanisms. The omnibus test is available for the wide range of data types and analyses that are supported by LDM. The omnibus test has been added to the R package LDM, which is available on GitHub at https://github.com/yijuanhu/LDM .