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The desire for a carbon-free planet has prompted a few experts to advocate for the most abundant energy known to us i.e Solar Energy. There are lots of solar powered energy systems in which the solar chimney power plant has been significant among passive methods in generating carbon free power. There are many attempts taken both experimentally as well as theoretically in developing efficient solar chimneys. This article reviews the major activities carried out in the various topographical regions covering significant research articles. In addition, the survey focuses on experimental research, understanding and recreational works, applications, and analogous work references, all of which are based on a broad range reference. Further, discussion on the research gap in each of the section is included with recommendations for future enhancement of this technology.

In recent times, Software Defined Networking (SDN) has gained immense popularity among communication networks due to its high network connectivity and security. But, owing to the lack of sufficient security schemes and dynamic access control, conventional techniques could be more efficient. Hence, this paper proposes a secure intrusion detection scheme for SDN utilizing LogishFTS-based Recurrent Neural Networks (LFTS-RNN) and Poisson Clumping Japanese Tree Frog Optimization Algorithm (PC-JTFOA). Initially, by using a username, password, and Signed Access Control (SAC), the registered users log in to the network. The Entropy Makwa-based Digital Signature Algorithm (EMDSA) is utilized to identify the attackers during the login process. Then, the Intrusion Detection System (IDS) is trained using the historical dataset. The critical features are extracted in IDS; subsequently, by using the PC-JTFOA, the optimal features are selected. Likewise, the selected features are inputted into the LFTS-RNN to recognize the intruders in the application plane. Moreover, the process of data security and load balancing is undergone by the data. Afterward, the balanced data is subjected to IDS to detect the attackers in the control plane. According to experimental outcomes, the proposed technique obtained a higher prominence with an accuracy of 98.35%. [ABSTRACT FROM AUTHOR]

In the present work, wastewater treatment plants and collection systems from Nagpur, Central India were studied for frequently occurring endocrine disrupting chemicals. All the EDCs, such as bisphenol A, butylparaben, and diethyl phthalate were present in all the collected samples with a detection frequency of 100%. Laboratory analysis revealed high bisphenol A, butylparaben, and diethyl phthalate levels, with mean concentrations of 79.10, 4.12, and 2.25 μg/L, respectively, in influent samples and 33.20, 1.62, and 0.77 μg/L, respectively, in the effluent wastewater. The distribution of the emergent pollutants among the sampling locations was found to be consistent with the physicochemical characteristics of wastewater, according to the component analysis, and the sites with the highest levels of contamination were also identified. Finally, an ecological risk assessment was carried out for aquatic biota in terms of risk quotients. The risk quotients for the target chemicals in effluent wastewater suggested a minimal impact, except for the target insect Chrironomus riparius, which had a risk quotient value of 33.20 for bisphenol A. [ABSTRACT FROM AUTHOR]