MEDICINAL PLANTS USED BY THE MANDAIS - A LITTLE KNOWN TRIBE OF BANGLADESH

The Mandais are a little known tribe of Bangladesh inhabiting the north central regions, particularly Tangail district of Bangladesh. Their population has been estimated to be less than 10,000 people. Although the tribe has for the most part assimilated with the mainstream Bengali-speaking population, they to some extent still retain their original tribal customs, including their traditional medicinal practices. Since this practice is also on the verge of disappearance, the objective of the present study was to conduct an ethnomedicinal survey among Mandai tribal practitioners to document their use of medicinal plants for treatment of various ailments. Four traditional practitioners were found in the exclusive Mandai-inhabited village of Chokchokia in Tangail district. Information was collected from the practitioners with the help of a semi-structured questionnaire and guided field-walk method. It was observed that the four traditional practitioners used a total of 31 plants distributed into 23 families for treatment. The various ailments treated included diabetes, low semen density, jaundice, gastrointestinal tract disorders (stomach ache, indigestion, dysentery, and diarrhea), leucorrhea, pain (rheumatic pain, joint pain), skin disorders, respiratory tract disorders (coughs, mucus, and allergy), debility, fever, and helminthiasis. From the number of plants used (seven), it appeared that gastrointestinal tract disorders formed the most common ailmen

OADC: An Obstacle-Avoidance Data Collection Scheme Using Multiple Unmanned Aerial Vehicles

Unmanned aerial vehicles (UAVs) are used widely for data collection in wireless sensor networks (WSNs). UAVs visit the sensors to collect the data. UAV-aided data collection is a challenging problem because different paths of a UAV, i.e., visiting orders of sensors, affect energy consumption and data delivery times. The problem becomes more difficult when there are obstacles in the path of the UAV. Thus, the UAV needs to take a detour to avoid them, resulting in different travel distances and times. Therefore, this study formulated the obstacle-aware path planning problem of UAVs, i.e., the obstacle-constrained distance minimization (OCDM) problem, as an integer linear programming problem (ILP) to minimize the total traveling distances of all UAVs while considering the UAVs’ flight time constraints. First, a possible detour-points-selection algorithm called vector rotation-angle-based obstacle avoidance (VRAOA) is proposed to find the detour points around each obstacle in the environment. Then, a genetic algorithm with VRAOA (GA w/VRAOA)is developed to find the trajectories of the UAVs, using the VRAOA and Dijkstra algorithm to find a detour path if there is an obstacle between any two sensors. Finally, simulations were performed for algorithm variants, where GA w/VRAOA outperformed others

OADC: An Obstacle-Avoidance Data Collection Scheme Using Multiple Unmanned Aerial Vehicles

Unmanned aerial vehicles (UAVs) are used widely for data collection in wireless sensor networks (WSNs). UAVs visit the sensors to collect the data. UAV-aided data collection is a challenging problem because different paths of a UAV, i.e., visiting orders of sensors, affect energy consumption and data delivery times. The problem becomes more difficult when there are obstacles in the path of the UAV. Thus, the UAV needs to take a detour to avoid them, resulting in different travel distances and times. Therefore, this study formulated the obstacle-aware path planning problem of UAVs, i.e., the obstacle-constrained distance minimization (OCDM) problem, as an integer linear programming problem (ILP) to minimize the total traveling distances of all UAVs while considering the UAVs’ flight time constraints. First, a possible detour-points-selection algorithm called vector rotation-angle-based obstacle avoidance (VRAOA) is proposed to find the detour points around each obstacle in the environment. Then, a genetic algorithm with VRAOA (GA w/VRAOA)is developed to find the trajectories of the UAVs, using the VRAOA and Dijkstra algorithm to find a detour path if there is an obstacle between any two sensors. Finally, simulations were performed for algorithm variants, where GA w/VRAOA outperformed others