Application Data Center Research Articles

Artwork Traceability and Anti-Counterfeiting Model Based on Block Chain Technology

The Chinese art market fostered the advancement of culture and spirituality. Nevertheless, false and counterfeit artwork is not uncommon. The art technology and security introduce the innovative method to determine Artwork Traceability and Anti-Counterfeiting. The aim of the work is to prevent the creation and circulation of fake or unauthorized copies of artworks. Because of the cryptographic security and immutability of block chains, it would be challenging to falsify artwork records or make accurate duplicates. This would foster confidence in the art market and shield collectors and artists from monetary losses and harm to their reputations. The data on mobile Application Data Center (ADC) platform and the cleansing the data using sub-aperture keystone transform matched filtering (SAKTMF) and the preprocessed data is fed into the Hierarchically Gated Recurrent Neural Network (HGRNN) classify the art work and Anti-Counterfeiting. The Bald Eagle Search Optimization (BES) Algorithm is used to optimize the weight parameter of HGRNN. The proposed model is implemented in MATLAB/ Simulink platform and the accuracy is compared to various existing approaches such as Dual-Branch Multi-Scale Feature Fusion network (DMF-Net), Region Convolutional Neural Network (R-CNN) and practical Byzantine fault tolerance (PBFT) algorithm. The gained results of the proposed HGRNN-BES method attains higher accuracy 97%, 96%, and 99%, higher precision 98%, 99%, and 97%.

The study of biochemical profile of cyst fluid and diffusion-weighted magnetic resonance imaging in differentiating hepatic hydatid cysts from liver simple cysts.

PurposeThe aim of this study was to investigate the value of biochemical profile of cyst fluid and diffusion‐weighted imaging (DWI) in differentiating hepatic hydatid cysts (HCs) from liver simple cysts.Materials and methodsForty‐six patients underwent MR imaging. Twenty‐nine patients had 29 hydatid cysts and 17 patients had liver simple cysts. Thirteen patients with hydatid cysts and seven patients with liver simple cysts were evaluated with cyst fluid biochemical analysis. The concentration of glucose, protein, calcium ion (Ca2+) electrolyte, macroscopic appearance, and parasitological sediment were evaluated in this study.ResultsIn the respect of biochemical analysis cyst fluid, the concentration of glucose and calcium ion of HCs was significantly higher than that of the liver simple cysts. In the respect of DWI, in the b 1000 s/mm2 value in respect of mean application data center (ADC) values, there was a statistically significant difference between HCs group (the mean value was (2.50±0.79)×10−3 mm/s2) and liver simple cysts group (the mean value was (2.92±0.66)×10−3 mm/s2). However, no statistically significant results were obtained in the ADC measurements of b 500 s/mm2 between two groups.ConclusionThe analysis of cyst fluid combined with the measurement of ADC values in the b 1000 s/mm2 value could be considered a promising parameter as an alternative to the differential diagnosis of hepatic hydatid cysts from liver simple cysts.