Traditional pretreatment methods have several disadvantages, including lower efficiency in terms of cellulose purity, high cost and incomplete separation of biomass components. Considering this, we compared different pretreatment processes and studied their effect on separation of biomass components. Effective delignification was achieved by alkali pretreatment with sodium hydroxide and ammonia. Among alkalis, sodium hydroxide treatment requires less severe process condition like lower concentration, temperature and pressure compared to ammonia pretreatment and produces enzyme amenable substrate, which made sodium hydroxide ideal for biomass pretreatment. Efforts were mounted to optimize sodium hydroxide pretreatment on wheat straw in terms of reaction temperature, reaction time and reagent concentration to produce enzymatically amenable substrate. The best experimental results were obtained when biomass was treated with 2% sodium hydroxide at 130°C for 30 minutes, which was found to extract lignin and significant amount of hemicellulose from biomass.

No abstract

No abstract

No abstract

<p>Recently remote sensing images have become more popular due to improved image quality and resolution. These images have been shown to be a valuable data source for road extraction applications like intelligent transportation systems, road maintenance, and road map making. In recent decades, the use of highly significant deep learning in automatic road extraction from these images has been a hot research area. However, fully automated and highly accurate road extractions from remote sensing images remain a challenge due to topology differences, complicated image backgrounds, and complex contexts. This paper proposes novel attention augmented convolution-based residual UNet architecture (AA-ResUNet) for road extraction, which adopts powerful features of self-attention mechanism and advantageous properties of residual UNet structure. The self-attention mechanism uses attention augmented convolutional operation to capture long-range global information; however, traditional convolution has a fundamental disadvantage: it only performs on local information. Therefore, we use the attention augmented convolutional layer as an alternative to standard convolution layers to obtain more discriminant feature representations. It allows to develop a network with fewer parameters. We also adopt improved residual units in standard ResUNet to the speedup training process and enhance the segmentation accuracy of the network. Experimental results on Massachusetts, DeepGlob Challenge, and UAV Road Dataset show that the AA-ResUNet performs well in road extraction, with Intersection over Union (IoU) (94.27%), lower trainable parameters (1.20 M), and inference time (1.14 sec). Comparative results on the proposed method have proven the supremacy or compatibility in road extraction with ten recently established deep learning approaches.</p>

No abstract

No abstract

The globe has not yet recovered from Coronavirus disease 2019 (COVID-19). The infection with the virus and its treatment can lead to a state of immunological aberration predisposing to many infections. Here we present this patient who was treated with steroids during COVID but later developed mucocutaneous nodular lesions and arthritis. This was initially treated as an autoimmune disease which was eventually diagnosed to be systemic histoplasmosis. There are few case reports on post-COVID histoplasmosis in HIV patients. However, there is a paucity of literature on non-HIV patients. We report this case as the treating physician and rheumatologist must be cognizant of the atypical infections which can mimic an autoimmune disease. As management differs in both, awareness can avoid morbidity for the patient.