Recently, Blockchain-based applications have become immensely popular because of limited reliance on a single entity, unlike a centralized system. However, reaching a consensus among blockchain networks is a challenging and vital aspect of blockchain-based applications. There are various types of blockchain networks for different kinds of application scenarios. Among all of them, the consensus algorithm is the most crucial part of reaching an agreement in the complex blockchain network. Over the years, researchers have focused on dealing with the challenges like distributed computing, storage, transaction speed, security, validity, interoperability, and many more. However, only some of them are appropriate for all domains. Therefore, this paper presents an extensive study of different types of consensus protocols used in existing blockchain solutions with the strength and limitations of each algorithm. We also provide an inherent comparison among different algorithms to understand consensus protocol selection better. Moreover, we investigate operational and interoperability issues in existing blockchain-based applications to understand challenges and provide recommendations for future developers.

Blockchain technology is emerging as a pivotal framework to enhance the security of internet-based systems, especially as advancements in machine learning (ML), artificial intelligence (AI), and cyber–physical systems such as smart grids and IoT applications in healthcare continue to accelerate. Although these innovations promise significant improvements, security remains a critical challenge. Blockchain offers a secure foundation for integrating diverse technologies; however, vulnerabilities—including adversarial exploits—can undermine performance and compromise application reliability. To address these risks effectively, it is essential to comprehensively analyze the vulnerability landscape of blockchain systems. This paper contributes in two key ways. First, it presents a unique layer-based framework for analyzing and illustrating security attacks within blockchain architectures. Second, it introduces a novel taxonomy that classifies existing research on blockchain vulnerability detection. Our analysis reveals that while ML and deep learning offer promising approaches for detecting vulnerabilities, their effectiveness often depends on access to extensive and high-quality datasets. Additionally, the layer-based framework demonstrates that vulnerabilities span all layers of a blockchain system, with attacks frequently targeting the consensus process, network integrity, and smart contract code. Overall, this paper provides a comprehensive overview of blockchain security threats and detection methods, emphasizing the need for a multifaceted approach to safeguard these evolving systems.

Adverse drug events represent a key challenge in public health, especially with respect to drug safety profiling and drug surveillance. Drug-drug interactions represent one of the most popular types of adverse drug events. Most computational approaches to this problem have used different types of data, such as drug chemical structure, information about protein targets, side effects, pathways, etc to predict potential interactions between drugs. In this work, we study the question of whether using just genetic information about the drugs can provide significant information about the potential safety profile for a given drug. We propose a novel neural network model to predict adverse drug events using only data about the protein sequence and protein structure associated with the drug targets. We compare the results with those from the state-of-the-art methods on this problem. Our results show that the proposed method is quite competitive, at times outperforming the state-of-the-art.

Adverse drug events represent a key challenge in public health, especially concerning drug safety profiling and drug surveillance. Drug-drug interactions represent one of the most popular types of adverse drug events. Most computational approaches to this problem have used different types of drug-related information utilizing different machine-learning algorithms to predict potential drug interactions. In this work, we focus on genetic information about the drugs, particularly the protein sequence and protein structure of protein targets in drug interaction networks, to predict potential drug interactions. We collected various drug information like drug-drug interaction (DDI), Drug attributes like drug active ingredients, protein targets, protein sequence, protein structure etc. We proposed a similarity-based Neural Network framework called protein sequence-structure similarity network (PS3N) and used this to predict novel DDI’s. The drug-drug similarities are computed using different categories of drug information based on multiple similarity metrics. Our method outperforms the state-of-the-art and achieves competitive results. Our performance evaluations on different datasets showed the predictive performance as follows: Precision 91%–98%, Recall 90%–96%, F1 Score 86%–95%, Area Under Curve (AUC) 88%–99%, and Accuracy 86%–95%. Our evaluation demonstrates the effectiveness of PS3N in predicting DDI’s, including the clinical significance of some new DDI’s discovered by the model.

Office hours are a core feature of CS courses. They provide a crucial vector for personalized instruction and individual support. Despite their importance, we the impact of these personal events has not been well assessed each office hour interaction is often hidden from instructors. We do not know if students actually understand the solution or the advice given during the interaction. This lack of knowledge presents a challenge to efforts geared at improving instructional practices and student outcomes. In this poster we report on the results of a survey analysis of office hour participants. Our results show that students and instructional staff had divergent assessments of the interactions with teaching staff overestimating the benefits more than 18.9% of the time. This work highlights detailed analyses of these results along with implications for teaching strategies.