Annual effective dose estimation of radon in drinking water sources of Nizampur basin, North Western Pakistan.

This study assessed radon (222Rn) levels in drinking water sources in the Nizampur basin and their potential health risks for the local community. We analyzed 48 water samples on-site using RAD7. Additionally, we measured pH, temperature (T), total dissolved solids (TDS), redox potential (ORP), and electrical conductivity (EC) with a multiparameter analyzer. Results showed pH, T, TDS, ORP, and EC ranging from 7.2 to 8, 17 to 26 °C, 333 to 1130 mg/l, -56 to 284 mV, and 469 to 2370 µS/cm. 222Rn levels varied significantly (0.7-107 Bq/l, mean 23 ± 21, median = 17 Bq/l), with about 65 % exceeding the EPA's limit of 11.1 Bq/l, indicating health risks likely due to local geological conditions. The annual effective doses for ingestion (EwIng) were 0.87 ± 0.01, 0.35 ± 0.006, and 0.13 ± 0.002 µSv/a for adults, infants, and children, respectively. Exposure risk via the inhalation (EwInh) route ranged from 1.75 to 270 µSv/a, with the highest risk in infants, followed by children and adults. Inhalation was the primary exposure route for all age groups. Further, spatial distribution maps and hotspot analysis suggested that the central region characterized by high structural deformation and favorable geology for radon emanation was the area of concern in terms of health risks.

Deformation Adjustment with Single Real Signature Image for Biometric Verification Using CNN.

Signature verification is the widely used biometric verification method for maintaining individual privacy. It is generally used in legal documents and in financial transactions. A vast range of research has been done so far to tackle different system issues, but there are various hot issues that remain unaddressed. The scale and orientation of the signatures are some issues to address, and the deformation of the signature within the genuine examples is the most critical for the verification system. The extent of this deformation is the basis for verifying a given sample as a genuine or forgery signature, but in the case of only a single signature sample for a class, the intra-class variation is not available for decision-making, making the task difficult. Besides this, most real-world signature verification repositories have only one genuine sample, and the verification system is abiding to verify the query signature with a single target sample. In this work, we utilize a two-phase system requiring only one target signature image to verify a query signature image. It takes care of the target signature's scaling, orientation, and spatial translation in the first phase. It creates a transformed signature image utilizing the affine transformation matrix predicted by a deep neural network. The second phase uses this transformed sample image and verifies the given sample as the target signature with the help of another deep neural network. The GPDS synthetic and MCYT datasets are used for the experimental analysis. The performance analysis of the proposed method is carried out on FAR, FRR, and AER measures. The proposed method obtained leading performance with 3.56 average error rate (AER) on GPDS synthetic, 4.15 AER on CEDAR, and 3.51 AER on MCYT-75 datasets.