Blockchain technology relies on cryptographic techniques that provide various advantages, such as trustworthiness, collaboration, organization, identification, integrity, and transparency. Meanwhile, data analytics refers to the process of utilizing techniques to analyze big data and comprehend the relationships between data points to draw meaningful conclusions. The field of data analytics in Blockchain is relatively new, and few studies have been conducted to examine the challenges involved in Blockchain data analytics. This article presents a systematic analysis of how data analytics affects Blockchain performance, with the aim of investigating the current state of Blockchain-based data analytics techniques in research fields and exploring how specific features of this new technology may transform traditional business methods. The primary objectives of this study are to summarize the significant Blockchain techniques used thus far, identify current challenges and barriers in this field, determine the limitations of each paper that could be used for future development, and assess the extent to which Blockchain and data analytics have been effectively used to evaluate performance objectively. Moreover, we aim to identify potential future research paths and suggest new criteria in this burgeoning discipline through our review. Index Terms— Blockchain, Distributed Database, Distributed Consensus, Data Analytics, Public Ledger.
This study examines the structural performance of concrete-encased pultruded Glass Fiber Reinforced Polymer (GFRP) I-sections with shear connections. It specifically focuses on how different parameters affect the latter’s ductility, flexural strength, and load-carrying capacity. The key variables studied include various shear connector types, spacing, and geometries, as well as the compressive strength of concrete and the properties of GFRP. The finite element modeling and experimental validation show that the shear connectors significantly improve the ductility, ultimate capacity, and load transmission efficiency. The present review emphasizes that the shear connectors greatly enhance the structural performance when they are prop
... Show More"1998 onwards, a span reporting 1000s of studies depicts the ever-increasing Schiff bases and their complexes applicability; this study genetically tests the research of the last 20 years. The variety of these molecules structural has made them obtainable for a so broad ambit for implementations of biological. They are eminent and because of this unique feature they find their position in the quantitative and qualitative calculation of metals in the aqueous medium. It demonstrated to be prominent catalysts and showed an enjoyable effect of fluorescence. Definitively, Schiff base fissures gotten situation of a unique during bio-experiments and in vitro to develop drugs with a large number of biological structures containing parasites
... Show MoreNonmissile penetrating spine injury (NMPSI) represents a small percent of spinal cord injuries (SCIs), estimated at 0.8% in Western countries. Regarding the causes, an NMPSI injury caused by a screwdriver is rare. This study reports a case of a retained double-headed screwdriver in a 37-year-old man who sustained a stab injury to the back of the neck, leaving the patient with a C4 Brown-Sequard syndrome (BSS). We discuss the intricacies of the surgical management of such cases with a literature review.
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The purpose of this paper is to apply different transportation models in their minimum and maximum values by finding starting basic feasible solution and finding the optimal solution. The requirements of transportation models were presented with one of their applications in the case of minimizing the objective function, which was conducted by the researcher as real data, which took place one month in 2015, in one of the poultry farms for the production of eggs
... Show MoreData scarcity is a major challenge when training deep learning (DL) models. DL demands a large amount of data to achieve exceptional performance. Unfortunately, many applications have small or inadequate data to train DL frameworks. Usually, manual labeling is needed to provide labeled data, which typically involves human annotators with a vast background of knowledge. This annotation process is costly, time-consuming, and error-prone. Usually, every DL framework is fed by a significant amount of labeled data to automatically learn representations. Ultimately, a larger amount of data would generate a better DL model and its performance is also application dependent. This issue is the main barrier for
Around 65 million individuals suffer from epilepsy worldwide, and when it is not properly treated, it is linked to higher rates of physical harm and mortality. Due to the requirement for long‐term therapy and the side effects of many medications, medication compliance is a significant issue. The purpose of this review was to summarize the findings of previous studies examining the quality of life (QOL), adherence, patient education, and medication knowledge, as well as the impact of a pharmacist‐led educational intervention. Additionally, to find out if these studies benefit epileptic patients, to find the appropriate method used to help them in all aspects of their lives, and to use these in future studies. A systematic and comprehensi
... Show MoreThe main challenge is to protect the environment from future deterioration due to pollution and the lack of natural resources. Therefore, one of the most important things to pay attention to and get rid of its negative impact is solid waste. Solid waste is a double-edged sword according to the way it is dealt with, as neglecting it causes a serious environmental risk from water, air and soil pollution, while dealing with it in the right way makes it an important resource in preserving the environment. Accordingly, the proper management of solid waste and its reuse or recycling is the most important factor. Therefore, attention has been drawn to the use of solid waste in different ways, and the most common way is to use it as an alternative
... Show MoreThe recent advancements in security approaches have significantly increased the ability to identify and mitigate any type of threat or attack in any network infrastructure, such as a software-defined network (SDN), and protect the internet security architecture against a variety of threats or attacks. Machine learning (ML) and deep learning (DL) are among the most popular techniques for preventing distributed denial-of-service (DDoS) attacks on any kind of network. The objective of this systematic review is to identify, evaluate, and discuss new efforts on ML/DL-based DDoS attack detection strategies in SDN networks. To reach our objective, we conducted a systematic review in which we looked for publications that used ML/DL approach
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