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Human activities, such as plant cultivation, enhance the activity concentrations (ACs) of radionuclides in the topsoil and have the potential to affect humans and the environment. The soil with a high radiation background not only acts as a source of continuous radiation exposure to humans but also as a medium of migration for the transfer of radionuclides to food crops. Some spectrometers can measure the AC of radionuclides in the soil, but long-term variation of AC is difficult to investigate. Therefore, developing methods for predicting the AC of radionuclides in agricultural soil is an interesting topic that many studies have focused on. In this work, a simple method was introduced to estimate long-term changes of some radioisotopes in the top layer of soil (0–20 cm). The estimation considered four major processes: fertilization, irrigation, vertical leaching, and plant uptake. The leaching rates (Rl) of radionuclides were predicted by their ACs in the soil water and the infiltration rate of water in the soil. The method was applied to the soils of five study fields in southern Vietnam, and the Rl values of 226Ra were between 7.4 Bq·m−2·y−1 and 60 Bq·m−2·y−1, while the rates of 210Po were between 22.1 and 111 Bq·m−2·y−1. For long-term assessment, it was found that 226Ra increases in sweat potato soil and tomato soil with annual rates (ΔC) of 0.99 Bq·kg−1·y−1 and 0.69 Bq·kg−1·y−1, respectively. The AC of 210Po was predicted to increase in some soils. The corresponding ΔC values for 210Po were 0.028 Bq·kg−1·y−1 in rice, 0.617 Bq·kg−1·y−1 sweet potato, and 0.099 Bq·kg−1·y−1 in tomato soils. The results imply that interventions are necessary to reduce the radioactivity accumulation in fields with high ΔC values. The effective methods can be changing the fertilizer types and the irrigation systems.

The design of analog computing systems requires significant human resources and domain expertise due to the lack of automation tools to enable these highly energy-efficient, high-performance computing nodes. This work presents the first automated tool flow from a high-level representation to a reconfigurable physical device. This tool begins with a high-level algorithmic description, utilizing either our custom Python framework or the XCOS GUI, to compile and optimize computations for integration into an Integrated Circuit (IC) design or a Field Programmable Analog Array (FPAA). An energy-efficient embedded speech classifier benchmark illustrates the tool demonstration, automatically generating GDSII layout or FPAA switch list targeting.