Misinformation such as on coronavirus disease 2019 (COVID-19) drugs, vaccination or presentation of its treatment from untrusted sources have shown dramatic consequences on public health. Authorities have deployed several surveillance tools to detect and slow down the rapid misinformation spread online. Large quantities of unverified information are available online and at present there is no real-time tool available to alert a user about false information during online health inquiries over a web search engine. To bridge this gap, we propose a web search engine misinformation notifier extension (SEMiNExt). Natural language processing (NLP) and machine learning algorithm have been successfully integrated into the extension. This enables SEMiNExt to read the user query from the search bar, classify the veracity of the query and notify the authenticity of the query to the user, all in real-time to prevent the spread of misinformation. Our results show that SEMiNExt under artificial neural network (ANN) works best with an accuracy of 93%, F1-score of 92%, precision of 92% and a recall of 93% when 80% of the data is trained. Moreover, ANN is able to predict with a very high accuracy even for a small training data size. This is very important for an early detection of new misinformation from a small data sample available online that can significantly reduce the spread of misinformation and maximize public health safety. The SEMiNExt approach has introduced the possibility to improve online health management system by showing misinformation notifications in real-time, enabling safer web-based searching on health-related issues.

To materialize the inherent opportunities of incoming sediment load, various interventions are being practiced for sediment management in rivers and delta systems. Broadly, the practiced methods can be divided into two categories: (i) management for accelerating siltation to enhance land reclamation and counterbalancing bank erosion (ii) management for de-siltation in the channels to maintain required navigational flow depth. The prerequisites for achieving the above targets at the same time are quite contradictory, as land reclamation requires more sediment, while maintaining navigational depth requires less sediment load. To address the aforementioned constraints, Bandal-like Structures (BLS), an indigenous nature-based solution, has proposed for redistributing the local flow-sediment regime to create a sediment deficit zone within the river side and a sediment surplus zone along the bankside, which can eventually be useful for the maintenance of navigational channels as well as bank stabilization. Based on research conducted over the last 20 years, this article discusses BLS's experiences in achieving both functions simultaneously. A well-documented case study of its application along a reach of the braided Jamuna River is one of many implemented BLS at various scales in the Ganges-Jamuna-Brahmaputra (GBM) system. Based on the performance of the implemented case study and existing indigenous knowledge, a hybrid approach integrating conventional and community sciences is proposed as a nature-based solution for sustainable sediment management in Bangladesh's river systems.

Purpose: With the remarkable capability to reach the public instantly, social media has become integral in sharing scholarly articles to measure public response. Since spamming by bots on social media can steer the conversation and present a false public interest in given research, affecting policies impacting the public’s lives in the real world, this topic warrants critical study and attention. Design/methodology/approach: We used the Altmetric dataset in combination with data collected through the Twitter Application Programming Interface (API) and the Botometer API. We combined the data into an extensive dataset with academic articles, several features from the article and a label indicating whether the article had excessive bot activity on Twitter or not. We analyzed the data to see the possibility of bot activity based on different characteristics of the article. We also trained machine-learning models using this dataset to identify possible bot activity in any given article. Findings: Our machine-learning models were capable of identifying possible bot activity in any academic article with an accuracy of 0.70. We also found that articles related to “Health and Human Science” are more prone to bot activity compared to other research areas. Without arguing the maliciousness of the bot activity, our work presents a tool to identify the presence of bot activity in the dissemination of an academic article and creates a baseline for future research in this direction. Research limitations/implications: We considered the features available from the Altmetric dataset. It can be exciting research to extract additional features about the authors of the article, the location of the publication, international collaboration and other demographic features of the authors to see the relation of these features with bot activity. Practical implications: Since public interest in scientific findings can shape the decisions of policymakers, it is essential to identify the possibility of bot activity in the dissemination of any given scholarly article. Without arguing whether the social bots are good or bad and without arguing about the validity of a scholarly article, our work proposes a tool to interpret the public interest in an article by identifying the possibility of bot activity toward an article. This work publishes the models and data generated through the study and provides a benchmark and guideline for future works in this direction. Originality/value: While the majority of the existing research focuses on identifying and preventing bot activity on social media, our work is novel in predicting the possibility of bot activity in the dissemination of an academic article using Altmetric metadata for the article. Little work has been performed in this specific area, and the models developed from our research give policymakers and the public a tool to interpret and understand the public interest in a scientific publication with appropriate caution.

Ultrasonic testing is a common Non-Destructive Inspection (NDI) method used in aerospace manufacturing. However, the complexity and size of the ultrasonic scans make it challenging to identify defects through visual inspection or machine learning models. Using computer vision techniques to identify defects from ultrasonic scans is an evolving research area. In this study, we used instance segmentation to identify the presence of defects in the ultrasonic scan images of composite panels that are representative of real components manufactured in aerospace. We used two models based on Mask-RCNN (Detectron 2) and YOLO 11 respectively. Additionally, we implemented a simple statistical pre-processing technique that reduces the burden of requiring custom-tailored pre-processing techniques. Our study demonstrates the feasibility and effectiveness of using instance segmentation in the NDI pipeline by significantly reducing data pre-processing time, inspection time, and overall costs.

While social media plays a vital role in communication nowadays, misinformation and trolls can easily take over the conversation and steer public opinion on these platforms. We saw the effect of misinformation during the COVID-19 pandemic when public health officials faced significant push-back while trying to motivate the public to vaccinate. To tackle the current and any future threats in emergencies and motivate the public towards a common goal, it is essential to understand how public motivation shifts and which topics resonate among the general population. In this study, we proposed an interactive visualization tool to inspect and analyze the topics that resonated among Twitter-sphere during the COVID-19 pandemic and understand the key factors that shifted public stance for vaccination. This tool can easily be generalized for any scenario for visual analysis and to increase the transparency of social media data for researchers and the general population alike.