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Pigments are one of the most significant secondary metabolites produced by microorganisms. The aim of the present study was to isolate and identify pigmentproducing bacteria from the Ratargul Swamp Forest (RSF) soil, which is the one and only fresh water swamp forest of Bangladesh. Soil samples were randomly collected from 10 different quadrates (10 m x 10 m) of RSF. The pH values of the soil samples were found to be strongly acidic and ranged between 4.71 and 5.48. Bacterial load of the samples ranged from 1.33×105 to 1.93×108 cfu/g, 6.05×106 to 9.07×107 cfu/g and from 1.16×107 to 1.61×108 cfu/g on nutrient agar (NA), peptone yeast-extract glucose (PYG) agar and Luria-Bertani (LB) agar media, respectively. Interestingly, both the highest and lowest bacterial counts were observed on NA, which was 1.93×108 cfu/g and 1.33×105 cfu/g, respectively. The isolates were found to produce various pigments like yellow, red, dark orange and sweet pink during their colony developments. A total of 71 bacterial isolates were obtained of which 11 were subjected to further study. All the selected bacteria were found to be rod shaped. Out of the 11 isolates, 9 were Gram-positive and 2 were Gramnegative. Provisionally identified potential pigment producing eight bacterial isolates were identified by using molecular marker. Seven of them were matched with their conventional identification up to generic level but conventionally identified Erwinia stewartii was found to be as Aeromonas sobria. Among the 11 isolates, 8 could produce three different types of pigments namely red, yellow and dark orange during in vitro pigment production. The isolated pigment producing bacteria could be used for better biotechnological application. Dhaka Univ. J. Biol. Sci. 31(1): 1-8, 2022 (January)

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The ability of smart devices to recognize their owners or valid users gains attention with the advent of widespread highly sensitive usage of these devices such as storing secret and personal information. Unlike the existing techniques, in this paper, we propose a very lightweight single-time user identification technique that can ensure a unique authentication by presenting a system nearto-impossible to breach for intruders. Here, we have conducted a thorough study over single-time usage data collected from 33 users. The study reveals several new findings, which in turn, leads us to a novel solution exploiting a new machine learning technique. Our evaluation confirms that the proposed solution operates with only 5% False Acceptance Rate (FAR) and only 6% False Rejection Rate (FRR) over the data collected from 33 users. We further evaluate the performance through comparing its performance with some existing machine learning techniques. Finally, we perform a real implementation of our proposed solution as a mobile application to conduct a rigorous user evaluation over 27 participants using three different devices in order to show how the solution works in practical situations. Outcomes of the user evaluation demonstrate as low as 0% FAR after letting intruders to mimic the actual user, which ensures extremely low probability of being breached. Moreover, we let 2 users to continuously use our application over 25 days in different states during their operation. Outcomes of this evaluation demonstrate as low as 1% FRR confirming the usability of our technique in long-term usage.

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