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The characterization of drug-target interactions is a key component of drug discovery, testing, and development. Affinity chromatography is one approach that can be used for this type of analysis. For instance, this may be done by using an immobilized target as a stationary phase and a drug as the applied solute. This review will discuss the various ways in which affinity chromatographic methods have been used to examine drug-target interactions, with an emphasis on high-performance methods. The general principles of this approach and factors to consider in its use for drug-target interaction analysis will first be examined. Methods based on zonal elution or frontal analysis for binding and competition studies will then be discussed. Various techniques for kinetic studies will next be considered, along with approaches that employ secondary binding agents and hybrid techniques. In each case, the general principles and theory of an approach will be given along with examples of its use in drug-target interaction studies. Advantages or limitations of each approach will be provided as well. This information should make it possible in the future to extend these techniques to other drug-target systems of interest in biomedical research and drug testing or development. 

Abstract Conventional lubricants face significant challenges in electric vehicle (EV) systems due to their low electrical conductivity and inability to mitigate tribo-electrification effects which can result in increased friction, wear, and electrical discharge damage under external electrification. Consequently, conductive lubricants like ionic liquids (ILs) have emerged as promising alternatives, offering enhanced compatibility with EV applications. This study investigated the tribological behavior of four phosphonium-based room temperature ionic liquids (PRTILs) with trihexyltetradecyl phosphonium [P6,6,6,14] or tributyltetradecyl phosphonium [P4,4,4,14] cations and saccharinate [Sacc] or benzoate [Benz] anions under electrified conditions, targeting potential EV applications. Physicochemical properties, including viscosity and ionic conductivity, were measured using a viscometer and a conductivity meter, while tribological properties were evaluated using an electrified mini-traction machine and an electrified rotary ball-on-disk setup. The results revealed that all the PRTILs exhibited superior tribological (friction and wear) performance than mineral oil with or without electrification. PRTILs with the [Sacc] anion feature a double aromatic ring structure, while those with the [Benz] anion feature a single aromatic ring structure. Under low electrification (10 mA), [P6,6,6,14][Sacc] outperformed [Benz]-based PRTILs, showing a lower coefficient of friction and wear due to their higher viscosity and lower ionic conductivity. Additionally, [P6,6,6,14][Sacc] showed a power loss lower than [P4,4,4,14][Sacc] but higher than [Benz]-based PRTILs under tribo-electrification. The addition of graphene nanoplatelets (GNPs) reduced the power loss of [P6,6,6,14][Sacc] by 24% by reducing the electric contact resistance. Overall, double-ring aromatic [P6,6,6,14][Sacc] demonstrated superior tribological performance, and GNP additives enhanced their power efficiency, offering a promising pathway for IL-based lubricant development for electrified conditions. 

The rapid advancement of Quantum Machine Learning (QML) has introduced new possibilities and challenges in the field of cybersecurity. Generative Adversarial Networks (GANs) have been used as promising tools in Machine Learning (ML) and QML for generating realistic synthetic data from existing (real) dataset which aids in the analysis, detection, and protection against adversarial attacks. In fact, Quantum Generative Adversarial Networks (QGANs) has great ability for numerical data as well as image data generation which have high-dimensional features using the property of quantum superposition. However, effectively loading datasets onto quantum computers encounters significant obstacles due to losses and inherent noise which affects performance. In this work, we study the impact of various losses during training of QGANs as well as GANs for various state-of-the-art cybersecurity datasets. This paper presents a comparative analysis of the stability of loss functions for real datasets as well as GANs generated synthetic dataset. Therefore, we conclude that QGANs demonstrate superior stability and maintain consistently lower generator loss values than traditional machine learning approaches like GANs. Consequently, experimental results indicate that the stability of the loss function is more pronounced for QGANs than GANs. 

Although software developers of mHealth apps are responsible for protecting patient data and adhering to strict privacy and security requirements, many of them lack awareness of HIPAA regulations and struggle to distinguish between HIPAA rules categories. Therefore, providing guidance of HIPAA rules patterns classification is essential for developing secured applications for Google Play Store. In this work, we identified the limitations of traditional Word2Vec embeddings in processing code patterns. To address this, we adopt multilingual BERT (Bidirectional Encoder Representations from Transformers) which offers contextualized embeddings to the attributes of dataset to overcome the issues. Therefore, we applied this BERT to our dataset for embedding code patterns and then uses these embedded code to various machine learning approaches. Our results demonstrate that the models significantly enhances classification performance, with Logistic Regression achieving a remarkable accuracy of 99.95%. Additionally, we obtained high accuracy from Support Vector Machine (99.79%), Random Forest (99.73%), and Naive Bayes (95.93%), outperforming existing approaches. This work underscores the effectiveness and showcases its potential for secure application development. 

In today’s fast-paced software development environments, DevOps has revolutionized the way teams build, test, and deploy applications by emphasizing automation, collaboration, and continuous integration/continuous delivery (CI/CD). However, with these advancements comes an increased need to address security proactively, giving rise to the DevSecOps movement, which integrates security practices into every phase of the software development lifecycle. DevOps security remains underrepresented in academic curricula despite its growing importance in the industry. To address this gap, this paper presents a handson learning module that combines Chaos Engineering and Whitebox Fuzzing to teach core principles of secure DevOps practices in an authentic, scenario-driven environment. Chaos Engineering allows students to intentionally disrupt systems to observe and understand their resilience, while White-box Fuzzing enables systematic exploration of internal code paths to discover cornercase vulnerabilities that typical tests might miss. The module was deployed across three academic institutions, and both pre- and post-surveys were conducted to evaluate its impact. Pre-survey data revealed that while most students had prior experience in software engineering and cybersecurity, the majority lacked exposure to DevOps security concepts. Post-survey responses gathered through ten structured questions showed highly positive feedback 66.7% of students strongly agreed, and 22.2% agreed that the hands-on labs improved their understanding of secure DevOps practices. Participants also reported increased confidence in secure coding, vulnerability detection, and resilient infrastructure design. These findings support the integration of experiential learning techniques like chaos simulations and white-box fuzzing into security education. By aligning academic training with realworld industry needs, this module effectively prepares students for the complex challenges of modern software development and operations.