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This research paper explores the evolution of voting systems, assessing vulnerabilities in traditional methods and investigating blockchain technology as a solution. Traditional voting systems encounter challenges concerning security, transparency, and accessibility, prompting a transition towards blockchain-based online voting. Blockchain, originating from cryptocurrencies, decentralizes control, thereby enhancing transparency, integrity, and security in elections. Real-world case studies from Estonia and West Virginia offer insights into implementation and successes. Ethical considerations, including privacy and consent, alongside societal impacts like inclusivity and democratization, are scrutinized. The proposed blockchain-based voting system prioritizes decentralization, tamper resistance, and interoperability. Security measures incorporate cryptographic techniques and multi-factor authentication. Transparency is assured through a publicly accessible blockchain ledger, facilitating an auditable voting process. The paper addresses challenges like voter accessibility and election anonymity, proposing mitigation strategies. Future directions entail integrating blockchain with AI and machine learning for interoperability and standardization. Collaboration among policymakers, technologists, and researchers is pivotal for advocating secure, transparent, and accessible blockchain-based voting systems, with the goal of fostering globally participatory elections. [ABSTRACT FROM AUTHOR]

The editorial explores the differing perspectives and priorities of anesthesiologists and gynecologists when dealing with fetal distress and urgent cesarean sections. Anesthesiologists focus on the mother's medical issues and administering appropriate anesthesia, while gynecologists are concerned with recognizing signs of fetal distress and ensuring safe delivery of the baby. Communication barriers and differing interpretations can lead to delays in decision-making, highlighting the importance of collaboration and understanding each other's priorities to minimize complications in fetal outcomes. Regular interdisciplinary meetings, joint training sessions, and simulation drills are recommended to enhance mutual understanding and streamline decision-making processes between the two specialties. [Extracted from the article]

The global COVID-19 pandemic has led to major changes in the surgical caseloads in the operation theatres across the world. Elective surgeries have been curtailed to a great extent and the number of emergency surgeries has risen. We conducted a study to assess the changes in the surgical caseloads in two tertiary care hospitals in India during the pandemic and to assess the changes in anaesthesia techniques used. A multicentre retrospective observational study conducted at two tertiary care centres located 1500 kms apart. Hospital A in a city with high incidence of COVID-19 and Hospital B with a lighter load of COVID-19 cases. Record of all major surgeries carried out between 01Jul 2020 and 31 Dec 2020 were compared with the surgeries carried out between 01 Jul 2019 and 31 Dec 2019. There was a decrease of almost 53% in elective surgical workload in hospital A and 71% in hospital B. The decrease in the cases was not similar across all specialities. There was a significant increase in the percentage of emergency surgeries from 11.1% to 24% in hospital A and from 22.1% to 29.8% in hospital B in the year 2020. A statistically significant increase in regional anaesthesia and neuraxial blockade was noted in the year 2020 in both the hospitals. During the COVID-19 pandemic, the number of elective surgical cases has significantly decreased in specialities like ophthalmology whereas there was no change in the surgical workload in specialities like obstetrics, oncology, orthopaedics, and neurosurgery. There was significant increase in the use of regional and neuraxial techniques of anaesthesia. [ABSTRACT FROM AUTHOR]

Through social media platforms and the internet, the world is becoming more and more connected, and producing enormous amounts of data. Also, the texts are collected from social media, newspapers, user reviews of products, company press releases, etc. The correctness of the classification is mainly dependent on the kind of words utilised in the corpus and the features utilised for classification. Hence, due to the increasing growth of text data on the Internet, the accurate organisation and management of text data has become a great challenge. Hence, in this research, an effective Invasive Weed Tunicate Swarm Optimization-based Hierarchical Attention Network (IWTSO-based HAN) is implemented for achieving categorisation of text. Here, the features are mined from the text and thereby the optimal features are acquired to perform the classification strategy. The incorporation of parametric features of each optimisation ensures the proposed method to increase the convergence of global solutions by improving the categorisation effectiveness. The proposed method obtained better performance for text classification with measures, like accuracy, True Positive Rate (TRP), True Negative Rate (TNR), precision, and False Negative Rate (FNR) with values of 92.4%, 92.4%, 94.1%, 95.4%, and 0.0758. [ABSTRACT FROM AUTHOR]