The colossal increase in environmental pollution and degradation, resulting in ecological imbalance, is an eye-catching concern in the contemporary era. Moreover, the proliferation in the development of smart cities across the globe necessitates the emergence of a robust smart waste management system for proper waste segregation based on its biodegradability. The present work investigates a novel approach for waste segregation for its effective recycling and disposal by utilizing a deep learning strategy. The YOLOv3 algorithm has been utilized in the Darknet neural network framework to train a self-made dataset. The network has been trained for 6 object classes (namely: cardboard, glass, metal, paper, plastic and organic waste). Moreover, for comparative assessment, the detection task has also been performed using YOLOv3-tiny to validate the competence of the YOLOv3 algorithm. The experimental results demonstrate that the proposed YOLOv3 methodology yields satisfactory generalization capability for all the classes with a variety of waste items.

A new metal-organic-framework (MOF) called UiO-66-NH-COCF3 was prepared using trifluoroacetamido functionalized terephthalic acid ligand. Powder X-ray diffraction (PXRD), infrared (IR) spectroscopy, thermogravimetric analysis (TGA) and Brunauer Emmett-Teller (BET) experiment were...

Covalent organic frameworks (COFs) having high specific surface area, tunable pore size and high crystallinity are mostly post modified following fluorinebased and complex synthetic approaches to achieve a bio-inspired liquid wettability, i.e. superhydrophobicity. Herein, a facile, non-fluorinated and robust chemical approach is introduced for tailoring the water wettability of a new COF-which was prepared through Schiff-base condensation reaction. A silane precursor was readily reacted with selected alkyl acrylates through 1,4-conjugate addition reaction, prior to grafting on the prepared C4-COF for tailoring different water wettability-including robust superhydrophobicity. The superhydrophobic C4-COF (SH-C4-COF) that displayed significantly enhanced (> 5 times; from 220 wt. % to 1156 wt. %) oil-absorption capacity, was extended to address the relevant challenges of "oil-in-water" emulsion separation, rapidly (< 1 minute) and repetitively (50 times) at diverse and harsh conditions.

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Rapid Urbanization, and other anthropogenic activities, have amplified the change in land-use transition from green space to heat emission in built-up areas globally. As a result, there has been an increase in the land surface temperature (LST) causing the Urban Heat Island (UHI) effect, particularly in large cities. The UHI effect poses a serious risk to human health and well-being, magnified in large developing cities with limited resources to cope with such issues. This study focuses on understanding the UHI effect in Kathmandu Valley (KV), Delhi, and Dhaka, three growing cities in South Asia. The UHI effect was evaluated by analyzing the UHI intensity of the city with respect to the surroundings. We found that the central urban area, of all three cities, experienced more heat zones compared to the peri-urban areas. The estimated average surface temperature ranged from 21.1 ∘C in March 2014 to 32.0 ∘C in June 2015 in KV, while Delhi and Dhaka experienced surface temperature variation from 29.7 ∘C in June 2017 to 40.2 ∘C in June 2019 and 23.6 ∘C in March 2017 to 33.2 ∘C in March 2014, respectively. Based on magnitude and variation of LST, highly built-up central KV showed heat island characteristics. In both Delhi and Dhaka, the western regions showed the UHI effect. Overall, this study finds that the UHI zones are more concentrated near the urban business centers with high population density. The results suggest that most areas in these cities have a rising LST trend and are on the verge of being UHI regions. Therefore, it is essential that further detailed assessment is conducted to understand and abate the impact of the temperature variations.