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The radon concentration of soil samples from several places in the Shaqlawa city/Iraqi Kurdistan area has been determined by using RAD7detector. The soil samples are gathered from different geological formations. Maximum and minimum radon concentrations were (88.3 ± 9.4) and (51.1 ± 7.1) Bq/m3 in the regions of Kawanian and Darband , respectively. The average radon concentration in soil samples from the city of Shaqlawa was (63.15 ± 16.94) Bq/m3, which was below the ICRP-recommended threshold (1991).

With the rapid development of the maritime industry, oily wastewater discharges and oil spills cause acute environmental challenges. Currently, cellulosic materials with switchable wettability have received increasing interest for oil–water separation. Moreover, work efficiency needs to be improved with a shorter response time. To realize these, dimethylaminoethyl methacrylate (DMAEMA) and methyltrimethoxysilane (MTMS) synthesized pH‐responsive copolymer (DMAEMA‐co‐MTMS) which grafted banana nanocellulose fiber made from wasted pseudostem to achieve the pH‐responsive aerogel (BCNA‐DcoM). BCNA‐DcoM exhibited hydrophilic/submerged lipophobicity under acidic conditions and hydrophobic/lipophilic under alkaline conditions. A response time of 3 s and a contact angle of 135° were shown when the mass ratio of banana nanocellulose suspension, MTMS, and DMAEMA‐co‐MTMS was 5:3:9. Moreover, BCNA‐DcoM possessed favorable mechanical friction properties, salt resistance (25% NaCl), compression resilience (50 cycles of compression at 60% strain), switchable wettability (>30 cycles), and highly efficient separation performance (>96%). The adsorption capacity of BCNA‐DcoM for oil is 80–184 times the initial weight of the aerogel based on the different oil densities. For practical applications, BCNA‐DcoM could continuously separate oil–water mixtures with the constant‐flow pump. The present work provides more possibilities for rapidly treating industrial oily wastewater with low‐cost and ecological banana pseudostem materials. [ABSTRACT FROM AUTHOR]

Real-time monitoring of transient stability has gained significant importance in the power system. This paper proposes an advanced hybrid Transient Stability Assessment (TSA) monitoring system, composed of a Convolutional Neural Network (CNN) and Random Forest (RF) classifier which captures the PMU measurements in real-time. CNN utilizes heatmaps for detecting the transient stability status which is hereby used as a feature extractor for the RF classifier. The IEEE-14 and the IEEE-39 bus systems are used as case studies in MATLAB. A wind turbine with a doubly-fed induction generator is also installed to inspect its impact on the power system stability. The efficiency of the system is tested under different contingency scenarios by implementing various faults on distinct locations under dynamic loading conditions. The test results verified that the proposed hybrid CNN-RF (Convolutional Neural Network-Random Forest) model exhibited excellent results in terms of monitoring accuracy, higher precision, and performance in the presence of noise. [ABSTRACT FROM AUTHOR]

Developing countries are facing many electricity crises such as low production, worst power outages, blackouts, and poor inadequate supplies. Most of their rural areas have no basic power infrastructure as it requires long transmission lines, which causes high installation, operation, maintenance costs and line losses issues. The people of these unconnected rural areas belong to low-income families and use kerosene oil, candles, small solar panels, and batteries to meet their basic electricity needs. As an alternative, renewable-based hybrid energy systems have enormous potential. It is also environmentally friendly and low-cost, sustainable, and promising solution to electrify rural off-grid areas. This research work focuses on the economic and optimal design of a renewable-based hybrid energy system for the rural off-grid area of Pakistan. HOMER (Hybrid Optimization of Multiple Energy Resources) software is used to perform techno-economic and environmental analysis. The optimization results show that the most economical and optimal configuration is PV/diesel generator with batteries having lower harmful emissions. Furthermore, the sensitivity analysis is performed to refine the results which show that the most optimal system will remain sustainable if variations occur in the sensitivity variables in the future. Moreover, the results and findings of this study can help the government to make effective policies and planning to provide reliable and affordable electricity to the off-grid areas, to improve the electrification rate in the country. [ABSTRACT FROM AUTHOR]