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Corrosion is known as the gradual destruction of materials, leading to structural integrity loss and deteriorates the surface function. Regarding sewage pipelines, corrosion is vital due to its substantial financial, health, and safety costs for society, and it is considered as one of the biggest problems facing water and wastewater infrastructure. Also, it is the primary cause of chemical property alteration, efficiency loss, life span reduction, etc. To overcome the resulting problems, various researches have been performed to understand not only the effective parameters leading to corrosion in sewer pipes but also monitoring the infrastructure conditions. Studies have depicted that developments in sensing systems to detect effective parameters in pipe corrosion such as temperature, H2S, and pH, have significantly reduced damage to the industrial equipment of sewage pipelines caused by corrosion. This paper presents a critical review of the effective factors resulting in sewer pipeline corrosion and discusses advanced sensing systems utilized for relevant monitoring. Also, microbiologically induced corrosion and effective factors are individually discussed. Moreover, various data analysis techniques adopted to evaluate outputs of the sensors for corrosion prediction have been explored. Finally, recommendations and future directions for improving sensing accuracy and robustness are detailed.

Purpose Changes in the entire health care system during COVID-19 epidemic have affected the management of patients with head and neck cancer and posed several clinical challenges for ENT surgeons. Therefore, the present study aimed to investigate the effect of COVID-19 on the stage and the type of surgical treatments used in laryngeal cancer (including total laryngectomy, supracricoid partial laryngectomy (SCPL) and transoral laser microsurgery (TLM)) and also to compare the results of April 2020 to April 2021 with the previous year. Materials and Methods This cross-sectional study was performed on all patients with a diagnosis of laryngeal cancer who underwent surgery in the tertiary care center from April 2020 to April 2021 and the year before the pandemic in the same time. Demographic, cancer stage, and treatment data of all patients were recorded and analysis in two groups. Results Patients referred at the time of the virus outbreak; 111 were male and 5 were female, and in the group of patients referred before COVID-19, 90 were male and 12 were female. The type of surgical treatment of laryngeal cancer, mean time elapsed from sampling to surgery, stage of disease and mean tumor volume was statistically significant differences in patients before and during the outbreak. Conclusion Patients who referred for diagnosis and treatment at the time of COVID-19 outbreak had more advanced stages of the disease and also the tumor volume was higher in them than patients who had referred before the outbreak. It is necessary to provide new solutions, education and treatment management for patients with laryngeal cancer in such pandemics.

The human genome encodes for a family of editing enzymes known as APOBEC3 (apolipoprotein B mRNA editing enzyme, catalytic polypeptide-like3). Several member of this family such as APOBEC3G, APOBEC3F and APOBEC3H haplotype II are active against viruses such as HIV. These enzymes induce C-to-U mutations in the negative strand of viral genomes, which leads to multiple G-to-A changes, referred to as ‘hypermutation’. Mutation by these enzymes are sequence context dependent in HIV genome, in which APOBEC3G preferentially mutates G within GG, TGG and TGGG content while other members mutate G within GA, TGA and TGAA. However, the same context between these enzymes and HBV virus have never been explored. In this study, we aim to identify mutational footprint of APOBEC3 enzymes in HBV genome. To do this, we perform a multivariable data analytics technique to investigate the motif preference and possible sequence hierarchy of mutation by APOBEC3 enzymes in a large number of full genome HBV sequences from a wide range of naturally infected patients. This enables us to differentiate between normal and hypermutated sequences in terms of the representation of mono- to tetra-nucleotide motifs and allows us to identify motifs associated with hypermutation by different APOBEC3 enzymes in the HBV genomes. Our analyses reveal that either APOPEC3 enzymes are not active against HBV virus or inducing G-to-A mutations by these enzymes are not sequence context dependent in HBV genome. Funding Statement: This work was supported by grants to HAR. HAR is currently supported by the UNSW Scientia Fellowship. Declaration of Interests: The authors declare that they have no financial or non-financial competing interests. Ethics Approval Statement: The authors stated: "Not applicable."

One of the essential tasks in Business Process Management (BPM) is Predictive Business Process Monitoring. This task aims to predict the behavior of an ongoing process based on the historical data stored in event logs. Since feed-forward neural networks do not consider the order of events for the prediction, they may not be helpful in predictive process monitoring. Recent research shows that using Recurrent Neural Networks such as LSTM and GRU may not be also helpful in predictive process monitoring. Because these networks use only the last hidden state as the context vector, and may lose some of past information, especially in long sequences. In addition, many existing approaches just use the activity name of each event as the representative of that event. In this context, they may ignore other events’ attributes in generating the feature vector. Some works have utilized these attributes simply by concatenating all of them together. While we need to use all event attributes to predict the next activity, it should be noted that not all of them are equally important. In this paper, we use two layers of attention mechanism on top of LSTM: (i) at the attribute level, to determine which attributes have more importance; and (ii) at the event level, to identify important events in predicting the next activity. Experimental evaluation of the real-world event logs showed that the use of hierarchical attention mechanisms in the proposed approach could effectively predict the next activity of an ongoing process.

Perceiving data can be challenging as the analysis goal is subjective. Storytelling approaches try to address this challenge by focusing on the research problem of understanding the data in general and, more particularly in curation, summarization, and presentation of the big data, i.e., a massive number of small data islands from personal, shared, and business data. In this context, summarization can facilitate understanding and surfacing insights that are embedded within the data. In this paper, we propose a general hierarchical personalized summarization framework, called Narrative Summaries (NARS), to improve the drawbacks of traditional summarization methods in various aspects. We proposed two variants of NARS: a semi-structured summarization approach (SNARS) and a fully-structured summarization approach (FNARS). The goal is to enable intelligent narrative construction of summaries based on the important features extracted from users' engagement. To achieve this goal, we propose a hierarchical structure to prevent users from being overwhelmed with less important information at first glance and to facilitate the selection process. Afterward, instead of providing a short static summary, we present an intelligent and interactive summarization approach to enable users to navigate through the hierarchy to gain more elaborated information upon request. Our approach aims to: (i) engage users in the summarization process, to guarantee the interactive speed even for extensive text collections, and (ii) eliminate the need for reference summaries, which is one of the most challenging issues in the summarization problem. The results demonstrate that our approach fabricates a summary that helps the users understand the topic better and faster compared to the existing state-of-the-art methods.

In collaborative content generation (CCG), such as publishing scientific articles, a group of contributors collaboratively generates artifacts available through a venue. The main concern in such systems is the quality. A remarkable range of research considers quality metrics partially when dealing with the quality of artifacts, contributors, and venues. However, such approaches have several drawbacks. One of the most notable ones is that they are not comprehensive in terms of the metrics to evaluate all entities, including artifacts, contributors, and venues. Also, they are vulnerable to potential attacks. In this paper, we propose a novel iterative definition in which the quality of artifacts, collaborators, and venues are defined interconnectedly. In our framework, the quality of an artifact is defined based on the quality of its contributors, venue, references, and citations. The quality of a contributor is defined based on the quality of his artifacts, collaborators, and the venues. Quality of a venue is defined based on both quality of artifacts and contributors. We propose a data model, formulations, and an algorithm for the proposed approach. We also compare the robustness of our approach against malicious manipulations with two well-known related approaches. The comparison results show the superiority of our method over other related approaches.

Blockchain-based platforms, particularly those based on permissioned blockchain, are increasingly popular in a broad range of settings. In addition to security and privacy concerns, organizations seeking to implement such platforms also need to consider performance, especially in latency- or delay-sensitive applications. Performance is generally less studied in comparison to security and privacy, and therefore in this paper we survey existing empirical performance evaluations of different permissioned blockchain platforms published between 2015 and 2019, using a comparative framework. The framework comprises ten criteria. We then conclude the paper with a number of potential future research directions.

In this paper, we introduce an approach to discriminate unconventional images and their intelligent filtering. As the target data to this issue are huge and consequently, a handling approach might potentially be a very time consuming one, one of the major challenges to be solved by this introduced approach is its ability for dealing with large-scale datasets. A deep neural network might be a good option to resolve this challenge. It can provide a good accuracy while dealing with huge databases. In the proposed approach, the new architecture is introduced using a combination of AlexNet and LeNet architectures. It uses convolutional, polling and fully-connected layers. The results are tested on two large-scale datasets. These tests show that the introduced architecture is more accurate than the other recently developed methods in identifying unconventional images. The proposed approach may be used in different applications such as intelligent filtering of unconventional images or medical images analysis.