Federated blockchain system (FBS) for the healthcare industry.

Blockchain is a distributed technology that introduced the well known Bitcoin cryptocurrency into action. Blockchain has been considered for research by many countries and industries. It is being applied in many fields such as the healthcare domain. Many companies started using Blockchain to increase the security and privacy of the Electronic Healthcare Records for their patients. The work in this paper discusses some existing healthcare problems and challenges. In addition, the paper reviews some related work models and provides a comparison that shows their objectives and limitations. Also, a proposed Federated Blockchain System (FBS) is introduced to provide solutions for these healthcare problems and elaborates the technical details of the system architecture. Moreover, the effectiveness of the system has been validated which showed an average of 68-100 ms for performing query operations and average of 0.944-19.041 s for performing writing operations on the system. Finally, a discussion of the system validation and future work are presented.

Ascorbic Acid Ameliorates Cardiac and Hepatic Toxicity Induced by Azithromycin-Etoricoxib Drug Interaction.

The complexity of prescribing safe and effective drug therapy is still challenging. Due to the increased number of medications taken by patients, the potential for drug-drug interactions has clinically important consequences. This study focuses on the potential drug-drug interaction between azithromycin and etoricoxib and the possibility of counteracting this adverse reaction by giving ascorbic acid intraperitoneally to male albino rats. Sixty adult male albino rats weighing 150-180 g were used. The rats were allocated into six equal groups. One group was a control, and the others were given azithromycin, etoricoxib, either alone or combination, with one group treated with ascorbic acid and the last group treated with the drug combination and ascorbic acid. Blood samples were collected for measuring AST, ALT, LDH, CK-MB, and troponin alongside antioxidant enzymes and histopathological examination for both liver and heart tissue. The results showed both hepatic and cardiac damage in azithromycin and etoricoxib groups represented by increasing levels of heaptoc enzymes (ALT, AST, LDH, CK-MB, and troponin) with declining antioxidant enzymes and elevation of malondialdehyde and the appearance of hepatic and cardiac toxicities. Upon administration, ascorbic acid ameliorated all the mentioned biochemical parameters. In conclusion, ascorbic acid has great antioxidant capacities and hepatic and cardiac ameliorative effects and can alleviate drug interaction toxicity.

Exploiting Reused-Based Sharing Work Opportunities in Big Data Multiquery Optimization with Flink.

Multiquery optimization is fundamental to retrieve data from different sources in a specific time frame to fulfill the credibility of the big data applications. To avoid high-cost inputs or outputs operations over large-scale data, exploiting sharing opportunities including (join, aggregation, and sort) is helpful to improve the performance of the multiple queries. Furthermore, considering the in-memory big data platforms such as Flink is essential to enhance the performance of the multiple queries. We have extended our previous proposed system, Multi-Query Optimization using Tuple Size and Histogram (MOTH), to exploit sharing work among multiquery, including join, aggregation, and sort. The comprehensive system, called the Join-Aggregation-Sort (JAS)-MOTH system, is used to minimize the data in-network movement time, that is, shuffle time needed to transfer intermediate data. The proposed system is introduced by developing two additional modules to investigate the sharing work; query explorer and JAS-MOTH optimizer, including the sort exploiter module. The JAS-MOTH system can exploit the shared explicit and implicit sorts among multiple sorts and aggregation queries. The proposed system refines the pipelined multiway join execution for multiple join queries by considering coarse-grained sharing data, join ordering, joining pipelining, and shared implicit sorts. Furthermore, it introduces an end-to-end multiway join optimizer over Flink. An elaborated experimental comparison of the naive and state-of-art techniques among a broader class of queries (i.e., join, aggregation, and sort) is presented. Given the consideration of the findings based on Flink, the proposed system has improved the query execution time regarding the naive and state-of-art techniques by 47% and 30%, respectively. For intermediate data size, the JAS-MOTH system has reduced the intermediate data size reduction concerning the naive and state-of-art techniques by 50% and 31% on average, respectively, over Hadoop-like infrastructures.

A novel no-sensors 3D model reconstruction from monocular video frames for a dynamic environment.

Occlusion awareness is one of the most challenging problems in several fields such as multimedia, remote sensing, computer vision, and computer graphics. Realistic interaction applications are suffering from dealing with occlusion and collision problems in a dynamic environment. Creating dense 3D reconstruction methods is the best solution to solve this issue. However, these methods have poor performance in practical applications due to the absence of accurate depth, camera pose, and object motion.This paper proposes a new framework that builds a full 3D model reconstruction that overcomes the occlusion problem in a complex dynamic scene without using sensors' data. Popular devices such as a monocular camera are used to generate a suitable model for video streaming applications. The main objective is to create a smooth and accurate 3D point-cloud for a dynamic environment using cumulative information of a sequence of RGB video frames. The framework is composed of two main phases. The first uses an unsupervised learning technique to predict scene depth, camera pose, and objects' motion from RGB monocular videos. The second generates a frame-wise point cloud fusion to reconstruct a 3D model based on a video frame sequence. Several evaluation metrics are measured: Localization error, RMSE, and fitness between ground truth (KITTI's sparse LiDAR points) and predicted point-cloud. Moreover, we compared the framework with different widely used state-of-the-art evaluation methods such as MRE and Chamfer Distance. Experimental results showed that the proposed framework surpassed the other methods and proved to be a powerful candidate in 3D model reconstruction.

SOOM: Sort-Based Optimizer for Big Data Multi-Query.

Mostly, sorting of data is a common operation in many applications, which causes the consumption of resources and thus leads to computation overheads. Regarding the context of Big Data multi-query, the shared sort operations are fairly large, which incur high-cost I/Os whether explicit or implicit. In particular, Big Data multi-query, including aggregation and sort operations, takes long execution time due to reshuffle of the same data multiple times using similar tasks. Therefore, exploiting the sharing data and the sharing sort opportunities of similar tasks can offer the possibility of reusing the previous results to optimize multi-query. For considering sharing data, our previous work, Multi-Query Optimization Using Tuple Size and Histogram (MOTH) system, has been introduced to consider the granularity of the sharing data opportunities among multi-query. However, time overheads regarding redundant data in-network movement (i.e., shuffling time to transfer intermediate data for sort operations) have not been considered. Therefore, the MOTH system has been extended to SOOM (Sort-Based Optimizer over MOTH) system to exploit sharing sort opportunities, including explicit sorts of sort queries and implicit sorts of aggregation queries. The proposed SOOM system consists of two additional modules to exploit sharing sort opportunities, namely query explorer and sort exploiter, which leverage our existing MOTH system to fulfill optimizing multiple aggregation and sort queries. The experimental evaluation has shown that the SOOM system outperforms the naive and the state-of-art techniques regarding query execution time among queries by 45% and 30%, respectively, while introducing maximal intermediate data size reduction by 67% and 61% in average, respectively, over Hadoop-like infrastructures.

A generalized architecture of quantum secure direct communication for N disjointed users with authentication.

In this paper, we generalize a secured direct communication process between N users with partial and full cooperation of quantum server. So, N - 1 disjointed users u1, u2, …, uN-1 can transmit a secret message of classical bits to a remote user uN by utilizing the property of dense coding and Pauli unitary transformations. The authentication process between the quantum server and the users are validated by EPR entangled pair and CNOT gate. Afterwards, the remained EPR will generate shared GHZ states which are used for directly transmitting the secret message. The partial cooperation process indicates that N - 1 users can transmit a secret message directly to a remote user uN through a quantum channel. Furthermore, N - 1 users and a remote user uN can communicate without an established quantum channel among them by a full cooperation process. The security analysis of authentication and communication processes against many types of attacks proved that the attacker cannot gain any information during intercepting either authentication or communication processes. Hence, the security of transmitted message among N users is ensured as the attacker introduces an error probability irrespective of the sequence of measurement.