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The world has been facing the COVID-19 pandemic since December 2019. Timely and efficient diagnosis of COVID-19 suspected patients plays a significant role in medical treatment. The deep transfer learning-based automated COVID-19 diagnosis on chest X-ray is required to counter the COVID-19 outbreak. This work proposes a real-time Internet of Things (IoT) framework for early diagnosis of suspected COVID-19 patients by using ensemble deep transfer learning. The proposed framework offers real-time communication and diagnosis of COVID-19 suspected cases. The proposed IoT framework ensembles four deep learning models such as InceptionResNetV2, ResNet152V2, VGG16, and DenseNet201. The medical sensors are utilized to obtain the chest X-ray modalities and diagnose the infection by using the deep ensemble model stored on the cloud server. The proposed deep ensemble model is compared with six well-known transfer learning models over the chest X-ray dataset. Comparative analysis revealed that the proposed model can help radiologists to efficiently and timely diagnose the COVID-19 suspected patients.

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Neutron-induced prompt gamma-ray analysis (PGA) was applied to seven meteorite samples (Allende, Zagami, Acfer 209, ALH77005, ALH84001, EET79001 and Neagari). Samples were irradiated in both the thermal neutron and the cold neutron guided beams of JRR-3M at JAERI. Multiple samples of an Allende standard powder were analyzed for Si using two different methods: (1) the comparison method, using a Si standard, and (2) the mono-standard method, using Fe as an internal reference element. The Si concentrations determined by these two methods are in good agreement with literature values. The analytical sensitivity for Si using the cold neutron guided beam is -14.3• higher than that for the thermal neutron guided beam. Other elements determined (B, Ca, Ti and S) also showed higher sensitivities using the cold neutron beam. The other meteorites studied showed some anomalous B and S values likely due to the effects of terrestrial weathering/contamination.

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Arsenic contamination of ground water is well understood while other environmental systems are rarely considered to be contaminated by arsenic. A vital issue is whether or not appreciable arsenic transmits through the food chain. Reportedly, ayurvedic herbal medicine products (AHMPs) manufactured in Asia were found to be contaminated by harmful level of Arsenic. This study was aimed to quantify the arsenic levels in water, herbal and soil samples collected from the same origin using highly accurate neutron activation analysis (NAA) technique. Harmful level of arsenic was detected in most of the water and soil samples. Moreover, a considerably harmful level of Arsenic was detected in herbal samples collected from the same origin. As a result, AHMPs manufactured in Asia might be contaminated by arsenic through arsenic contaminated herb plants.

Beach sands harbor appreciable amount of radioactive elements such as zircon which contains some important and radioactive elements (e.g. uranium, thorium). Very limited quantitative information is available of such elements in the sand of the beaches of Cox's Bazar in Bangladesh. The present study presents application of Neutron Activation Analysis (NAA) to quantify uranium and thorium elemental concentration in zircon assemblages from beach placers of Cox's Bazar. Zircon was separated from beach sands in a mineral processing pilot plant. The separated zircon assemblages were examined by a polarizing petrographic microscope and X-ray diffraction techniques and found that it contained more than 75% pure zircon. Thereafter, uranium and thorium concentrations in zircon were measured by NAA and revealed that zircon contained 94-141 ppm uranium and 127-506 ppm thorium. Therefore, this noticeable amount of uranium and thorium in zircon should be taken in consideration during any mining attempt of the placer minerals present in Cox's Bazar beaches.