A Review on Modern Distributed Computing Paradigms: Cloud Computing, Jungle Computing and Fog Computing

The development of medical assisting tools based on artificial intelligence advances is essential in the global fight against COVID-19 outbreak and the future of medical systems. In this study, we introduceai-corona, a radiologist-assistant deep learning framework for COVID-19 infection diagnosis using chest CT scans. Our framework incorporates an EfficientNetB3-based feature extractor. We employed three datasets; the CC-CCII set, the MasihDaneshvari Hospital (MDH) cohort, and the MosMedData cohort. Overall, these datasets constitute 7184 scans from 5693 subjects and include the COVID-19, non-COVID abnormal (NCA), common pneumonia (CP), non-pneumonia, and Normal classes. We evaluateai-coronaon test sets from the CC-CCII set, MDH cohort, and the entirety of the MosMedData cohort, for which it gained AUC scores of 0.997, 0.989, and 0.954, respectively. Our results indicatesai-coronaoutperforms all the alternative models. Lastly, our framework’s diagnosis capabilities were evaluated as assistant to several experts. Accordingly, We observed an increase in both speed and accuracy of expert diagnosis when incorporatingai-corona’s assistance.

In this work, a high-throughput bitsliced AES implementation is proposed, which builds upon a new data representation scheme that exploits the parallelization capability of modern multi/many-core platforms. This representation scheme is employed as a building block to redesign all of the AES stages to tailor them for multi/many-core AES implementation. With the proposed bitsliced approach, each parallelization unit processes an unprecedented number of thirty-two 128-bit input data. Hence, a high order of prallelization is achieved by the proposed implementation technique. Based on the characteristics of this new implementation model, the ShiftRows stage can be implicitly handled through input rearrangement and is simplified to the point where its computing process can be neglected. In this implementation, costly Byte-wise operations are performed through register shift and swapping. In addition, the need for look-up table based I/O operations, which are used by the Substitute Bytes stage is eliminated through using S-box logic circuit. The S-box logic circuit is optimized to simultaneously process 32 chunks of 128-bit input data. We develop high-throughput CTR and ECB AES encryption/decryption on 6 CUDA-enabled GPUs, which achieve 1.47 and 1.38 Tbps of encryption throughput on V100 GPU, respectively.

The present study examined the effect of chitosan (Ch) and combination of chitosan and Thymus vulgaris essential oil (Ch+T) on the quality and shelf life of butterfly-shaped rainbow trout stored under refrigerated condition (5 ± 1°C) for 15 days. The treatments in the present study were Ch (chitosan: 2% w/v) and Ch+T (chitosan: 2% w/v and Thymus vulgaris essential oil: 1% w/v). The shelf life of the samples were determined using microbiological (total psychrotrophic count), chemical—pH, thiobarbituric acid (TBA), and total volatile basic nitrogen (TVB-N)—and textural analyses. Results showed that total psychrotrophic count decreased significantly (p < 0.05) using Ch+T coating during the entire period of storage. The treatment Ch+T resulted in lower pH, TBA, and TVB-N values in comparison with the other samples. Based mainly on our data, Ch+T treatment extended the product's shelf life by approximately 6 days in comparison with the uncoated control samples.