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Students spend a large portion of their time in school. In this broadened time of sitting, poor fitting furniture can cause various types of musculoskeletal disorders and discomforts. Thus, it is crucial to use anthropometric data to coordinate the arrangement of school furniture. To fulfill this perception, a survey has been conducted in 10 primary schools in Khulna, Bangladesh. Anthropometric measurements were accumulated from 300 students of these primary schools. Seven furniture dimensions were measured and fifteen anthropometric measurements were taken and they were compared to identify potential mismatch. A significant degree of mismatch was found between furniture and student anthropometric measurements. The results highlighted that desktop height and seat height were found too high and seat width was too small for all of the students. The paper also proposes furniture dimensions, which reduce mismatch percentage of students ranging from 90% to 10%.

Exploiting opportunistic contacts between mobile devices to enable deployment of real applications through reliable and efficient data transfers poses a significant research challenge. Indeed, accurate prediction of contact volume, defined as the maximum amount of data transferable during a contact, can improve performance of deployments. However, existing schemes for estimating contact volume that make use of preconceived patterns or contact time distributions may not be applicable in uncertain environments. In this paper, we propose a novel scheme called PCV that predicts contact volume in soft real-time to enable efficient and reliable data transfers in opportunistic networks. An Android Application that learns data rate profiles has been developed to facilitate PCV. In addition, an analytical model has been developed to depict variable data rates between mobile devices. Extensive simulations are carried out on both synthetic and real world mobility traces to validate the usefulness of PCV. Experimental results show the effectiveness of our approach in terms of reliable data transfers.

Injuries during cultivation of land are the significant causes of recession for an agricultural country like Bangladesh. Thousands of tools are used in agricultural farm having much probability of getting injury at their workplaces. For the injury prevention, proper hand tool designs need to be recommended with ergonomic evaluations. This paper represents the main causes of agricultural injuries among the Bangladeshi farmers. Effective interventions had been discussed in this paper to reduce the rate of injury. This study was carried out in the Panchagarh district of Bangladesh. Data on 434 agricultural injuries were collected and recorded. About 67% injuries of all incidents were due to hand tools, and the remaining 33% were due to machinery or other sources. Though most of the injuries were not serious, about 22% injuries were greater than or equal to AIS 2 (Abbreviated Injury Scale). The practical implication of this study is to design ergonomically fit agricultural hand tools for Bangladeshi farmers in order to avoid their injuries.

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Opportunistic networks have so far been seen mostly as delay tolerant in nature. As a result, real-time and soft real-time applications, which demand consistent and instant connectivity of the network, are usually considered intangible in opportunistic communication environments. In this paper we seek to reveal the inherent connected virtual backbone in an opportunistic network through the consociation of the neighbors in the network. This backbone can pave the way for designing an architecture for real-time social applications. The backbone may change in terms of time, location and crowd density. Experimenting on real world as well as synthetic human mobility traces and pause times, we first structure the pattern of human halt durations at popular places. Infusing this pattern, we then prove the existence of the intrinsic backbone in those networking environments, where people show regularity in their movements. Applying graph-theoretic concepts like Minimum Connected Dominating Set and Unit Node Weighted Steiner Tree we further optimize and ensure the robustness of the backbone. Simulation results show the effectiveness of our approach in exposing a newer dimension in the form of real time interaction prospects in opportunistic networks.