Evaluation of radioactivity in soil and rock samples from an undiscovered sea beach in the southeastern coastline of Bangladesh and associated health risk.

This study marks the first-ever assessment of radiological hazards linked to the sands and rocks of Patuartek Sea Beach, situated along one of the world's longest sea beaches in Cox' Bazar of Bangladesh. Through the utilization of an HPGe detector, a comprehensive analysis of the activity concentrations of 226Ra, 232Th, and 40 K was conducted, and their activity ranged from 7 to 23 Bq/kg, 9-58 Bq/kg, and 172-340 Bq/kg, respectively, in soils, and 19-24 Bq/kg, 27-39 Bq/kg, and 340-410 Bq/kg, respectively, in rocks. Some sand samples exhibited elevated levels of 232Th, while the rock samples displayed higher levels of 40 K compared to the global average. The radiological hazard parameters were assessed, and no values surpassed the recommended limits set by several international organizations. Hence, the sands and rocks of Patuartek sea beach pose no significant radiological risk to the residents or tourists. The findings of this study provide crucial insights for the development of a radiological baseline map in the country, which is important due to the commissioning of the country's first nuclear power plant Rooppur Nuclear Power Plant. The data may also stimulate interest in the rare-earth minerals present in the area, which is important for the electronics industry, thorium-based nuclear fuel cycles.

Nonlinear dynamic model for visual object tracking on Grassmann manifolds with partial occlusion handling.

This paper proposes a novel Bayesian online learning and tracking scheme for video objects on Grassmann manifolds. Although manifold visual object tracking is promising, large and fast nonplanar (or out-of-plane) pose changes and long-term partial occlusions of deformable objects in video remain a challenge that limits the tracking performance. The proposed method tackles these problems with the main novelties on: 1) online estimation of object appearances on Grassmann manifolds; 2) optimal criterion-based occlusion handling for online updating of object appearances; 3) a nonlinear dynamic model for both the appearance basis matrix and its velocity; and 4) Bayesian formulations, separately for the tracking process and the online learning process, that are realized by employing two particle filters: one is on the manifold for generating appearance particles and another on the linear space for generating affine box particles. Tracking and online updating are performed in an alternating fashion to mitigate the tracking drift. Experiments using the proposed tracker on videos captured by a single dynamic/static camera have shown robust tracking performance, particularly for scenarios when target objects contain significant nonplanar pose changes and long-term partial occlusions. Comparisons with eight existing state-of-the-art/most relevant manifold/nonmanifold trackers with evaluations have provided further support to the proposed scheme.