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The emergence of several complex environmental pollutants from various industrial activities has posed a global challenge for ecorestoration and sustainable development. The use of Plant Growth-Promoting Rhizobacteria (PGPR) for the remediation of soil pollution has gained recognition as a promising biotechnological tool for sustainable development. Recent advances in metagenomic approaches have allowed for the detection of rhizospheric bacterial communities from polluted soils revealing their contributions to the degradation of complex pollutants through the production of potential enzymes as a novel technique. In addition, protein/enzyme engineering and the fate of heavy metals accumulation in plant tissues, through bacterial-assisted phytoremediation, has expanded the role of PGPR in soil reclamation by improving their efficacy, understanding their mechanisms of action, and integrating them with other innovative approaches been reported as advance technique. These advancements give strong evidence for more efficient and sustainable soil remediation strategies, aiding in the restoration of contaminated soil and ensuring environmental sustainability. These techniques and PGPR attributes will provide a green technology for environmental safety.

Smart buildings are the need of the day with increasing demand-supply ratios and considerable deficiency to generate them. In any modern nonindustrial infrastructure, these demands mainly comprise thermostatically controlled loads (TCLs), which can be maneuvered. TCLs, such as air-conditioners, heaters, and refrigerators, are ubiquitous and their operating times can be controlled to achieve desired aggregate power. This power aggregation, in turn, helps achieve load management targets and thereby serves as an ancillary service (AS) to the power grid. In this work, a distributed averaging protocol is used to achieve the desired power aggregate set by the utility using steady-state desynchronization. The results are verified using a computer program for a homogeneous and heterogeneous population of TCLs. Furthermore, a load following scenario has been implemented using the utility as a reference. Apart from providing a significant AS to the power grid, the proposed idea also helps reduce the amplitude of power system oscillations imparted by the TCLs. Hardware-based results are obtained to verify its implementation feasibility in real-time.

Spin pumping has significant implications for spintronics, providing a mechanism to manipulate and transport spins for information processing. Understanding and harnessing spin currents through spin pumping is critical for the development of efficient spintronic devices. The use of a magnetic insulator with low damping enhances the signal-to-noise ratio in crucial experiments such as spin-torque ferromagnetic resonance (FMR) and spin pumping. A magnetic insulator coupled with a heavy metal or quantum material offers a more straightforward model system, especially when investigating spin-charge interconversion processes to greater accuracy. This simplicity arises from the absence of unwanted effects caused by conduction electrons unlike in ferromagnetic metals. Here, we investigate the spin pumping in coupled ferrimagnetic (FiM) Y3Fe5O12 (YIG)/Tm3Fe5O12 (TmIG) bilayers combined with heavy-metal (Pt) using the inverse spin Hall effect. It is observed that magnon transmission occurs at both of the FiMs FMR positions. The enhancement of spin pumping voltage (Vsp) in the FiM garnet heterostructures is observed. The plausible reason might be the interfacial exchange coupling between FiMs. The modulation of Vsp is achieved by tuning the bilayer structure. Further, the spin mixing conductance for these coupled systems is found to be ≈ 10 18 m−2. Our findings describe a coupled FiM system for the investigation of magnon coupling providing opportunities for magnonic devices. [ABSTRACT FROM AUTHOR]

Boosting algorithms is one of the greatest tool in machine learning and over the years it has various versions and they all have their own identities. The main objective of the paper is to provide comprehensive study on various algorithms and try to solve a challenging classification problem of customer churn and observe the result of these algorithms too see which of them work the best in the raw state possible. In this paper of we used a dataset “Telecommunication Customer Churn Dataset” and we will few preprocessing to level the ground for all our models/algorithms. Finally we will run some test like accuracy, cross validation to find the best performing model among the boosting algorithm which we will use. [ABSTRACT FROM AUTHOR]