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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. 

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. 

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. 

Devices made from thin films of halide perovskites are advancing due to their potential in photovoltaic and optoelectronic applications, largely attributed to their energy level tunability, which can be achieved... 

Transparent conductive oxides (TCOs) are a high-performance material system that could enable new wearable sensors and electronics, but traditional fabrication methods face scalability and performance challenges. In this work, we utilize liquid metal printing to produce ultrathin two-dimensional (2D) indium tin oxide (ITO) films with superior microstructural, optical, and electrical properties compared to conventional techniques. We investigate the dynamics of grain growth and its influence on conductivity and the optical properties of 2D ITO, demonstrating the tunability through annealing and multilayer deposition. Additionally, we develop Au-decorated transparent electrodes, showcasing their adhesion and flexibility, low contact impedance, and biocompatibility. Leveraging the transparency of these electrodes, we enable enhanced simultaneous multimodal biosignal acquisition by integrating biopotential-based methods, such as electrocardiogram (ECG) or bioimpedance sensing (e.g., impedance plethysmography, IPG), with optical modalities like photoplethysmography (PPG). This study establishes CLMP-fabricated flexible 2D TCOs as a versatile platform for advanced bioelectronic systems and multimodal diagnostics. 

With the rapid growth of technology, accessing digital health records has become increasingly easier. Especially mobile health technology like mHealth apps help users to manage their health information, as well as store, share and access medical records and treatment information. Along with this huge advancement, mHealth apps are increasingly at risk of exposing protected health information (PHI) when security measures are not adequately implemented. The Health Insurance Portability and Accountability Act (HIPAA) ensures the secure handling of PHI, and mHealth applications are required to comply with its standards. But it is unfortunate to note that many mobile and mHealth app developers, along with their security teams, lack sufficient awareness of HIPAA regulations, leading to inadequate implementation of compliance measures. Moreover, the implementation of HIPAA security should be integrated into applications from the earliest stages of development to ensure data security and regulatory adherence throughout the software lifecycle. This highlights the need for a comprehensive framework that supports developers from the initial stages of mHealth app development and fosters HIPAA compliance awareness among security teams and end users. An iOS framework has been designed for integration into the Integrated Development Environment(IDE), accompanied by a web application to visualize HIPAA security concerns in mHealth app development. The web application is intended to guide both developers and security teams on HIPAA compliance, offering insights on incorporating regulations into source code, with the IDE framework enabling the identification and resolution of compliance violations during development. The aim is to encourage the design of secure and compliant mHealth applications that effectively safeguard personal health information. 

Large Language Models (LLMs) have demonstrated exceptional capabilities in the field of Artificial Intelligence (AI) and are now widely used in various applications globally. However, one of their major challenges is handling high-concurrency workloads, especially under extreme conditions. When too many requests are sent simultaneously, LLMs often become unresponsive which leads to performance degradation and reduced reliability in real-world applications. To address this issue, this paper proposes a queue-based system that separates request handling from direct execution. By implementing a distributed queue, requests are processed in a structured and controlled manner, preventing system overload and ensuring stable performance. This approach also allows for dynamic scalability, meaning additional resources can be allocated as needed to maintain efficiency. Our experimental results show that this method significantly improves resilience under heavy workloads which prevents resource exhaustion and enables linear scalability. The findings highlight the effectiveness of a queue-based web service in ensuring LLMs remain responsive even under extreme workloads. 

The layered transition metal chalcogenides MCrX₂ (M = Ag, Cu; X = S, Se, Te) are of interest for energy storage because chemically Li-substituted analogs were reported as superionic Li⁺ conductors. The coexistence of fast ion transport and reducible transition metal and chalcogen elements suggests that this family may offer multifunctional capability for electrochemical storage. Here, we investigate the electrochemical reduction of AgCrSe₂ and CuCrSe₂ in non-aqueous Li- and Na-ion electrolytes using electrochemical measurements coupled with ex situ characterization (scanning electron microscopy, energy-dispersive spectroscopy, X-ray diffraction, and X-ray photoelectron spectroscopy). Both compounds delivered high initial specific capacities (~ 560 mAh/g), corresponding to 6.64 and 5.73 Li⁺/e^- per formula unit for AgCrSe₂ and CuCrSe₂, respectively. We attribute this difference to distinct reduction pathways: 1) Li⁺ intercalation to form LiCrSe₂ and extruded Ag or Cu, 2) conversion of LiCrSe₂ to Li₂Se, and 3) formation of an Ag-Li alloy at the lowest potential, operative only in AgCrSe₂. Consistent with this proposed mechanism, step 3 was absent during reduction of AgCrSe₂ in a Na-ion electrolyte since Ag does not alloy with Na. These results demonstrate the complex reduction chemistry of MCrX₂ in the presence of alkali ions, providing insights into the use of MCrX₂ materials as alkali ion superionic conductors or high capacity electrodes for lithium or sodium-ion type batteries. 

Federated Survival Analysis (FSA) is an emerging Federated Learning (FL) paradigm that enables training survival models on decentralized data while preserving privacy. However, existing FSA approaches largely overlook the potential risk of bias in predictions arising from demographic and censoring disparities across clients' datasets, which impacts the fairness and performance of federated survival models, especially for underrepresented groups. To address this gap, we introduce FairFSA, a novel FSA framework that adapts existing fair survival models to the federated setting. FairFSA jointly trains survival models using distributionally robust optimization, penalizing worst-case errors across subpopulations that exceed a specified probability threshold. Partially observed survival outcomes in clients are reconstructed with federated pseudo values (FPV) before model training to address censoring. Furthermore, we design a weight aggregation strategy by enhancing the FedAvg algorithm with a fairness-aware concordance index-based aggregation method to foster equitable performance distribution across clients. To the best of our knowledge, this is the first work to study and integrate fairness into Federated Survival Analysis. Comprehensive experiments on distributed non-IID datasets demonstrate FairFSA's superiority in fairness and accuracy over state-of-the-art FSA methods, establishing it as a robust FSA approach capable of handling censoring while providing equitable and accurate survival predictions for all subjects.