Authors - Vishnu Kumar Abstract - Heart disease remains a leading cause of mortality in the United States, responsible for approximately 1 in 5 deaths in 2022. Modifiable behavioral and lifestyle factors, such as smoking, physical activity, and diet, play a critical role in cardiovascular risk. This study applies a machine learning (ML) approach to predict heart disease risk in the U.S. using data from the 2022 Behavioral Risk Factor Surveillance System (BRFSS). Three ML based classification models were developed using ten key behavioral and lifestyle features: general health perception, days of poor physical and mental health, time since the last checkup, physical activity engagement, average sleep duration, smoking status, e-cigarette use, body mass index (BMI), and alcohol consumption. Among the three ML based classification models, XGBoost exhibited superior performance, achieving an F1-score of 0.92 with balanced precision and recall across both classes. SHAP (Shapley Additive Explanations) was then used to identify the impact of behavioral and lifestyle factors on heart disease risk. Global SHAP analysis revealed that general health, poor mental health, and BMI were the most influential features affecting heart disease risk. Local SHAP analysis showed that the importance of individual features varied across different observations, with factors such as: time since the last checkup, and smoking status significantly influencing heart disease risk for certain individuals. These findings demonstrate the potential of explainable ML techniques to identify actionable, personalized cardiovascular risk factors. The insights gained can help healthcare providers tailor interventions and prevention strategies, prioritize high-risk individuals for early detection, and allocate resources more effectively to reduce the burden of heart disease.
Authors - Fawzy Alsharif, Hasan Kaan Aldemir, Akay Deliorman Abstract - This paper presents image processing techniques for detecting eye diseases such as Vessel Tortuosity (VT), Glaucoma, Central Serous Retinopathy (CSR), and Diabetic Retinopathy (DR). The system supports early symptom detection, condition monitoring, and timely intervention. For VT, green channel extraction, Gaussian blurring, and Otsu thresholding isolate vessels, followed by morphological operations and thinning for curvature analysis. In Glaucoma, contrast enhancement and multi-level Otsu thresholding segment the optic disc and cup, enabling Cup-to-Disc Ratio calculation. For CSR, green channel processing and Gaussian blurring highlight fluid accumulation. In DR, lesion visibility is improved through green channel extraction, blurring, and morphological filtering. This integrated approach enhances image clarity and segmentation, achieving 97%–99% accuracy in early disease detection.
Authors - Doaa Abdelrahman, Heba Aslan, Mahmoud M. Nasreldin, Ghada Elkabbany, Mohamed Rasslan Abstract - The Bitcoin economy has grown significantly and rapidly, reaching an estimated market capitalization of around $1.87 trillion. As a type of cryptocurrency—essentially digital money—Bitcoin enables direct transactions between users without relying on a central authority or intermediary. These transactions are validated by network participants using cryptographic techniques and are permanently stored in a decentralized public ledger known as the blockchain. New Bitcoins are introduced into circulation through a process that is called mining, and they can be traded for conventional currencies, goods, or services. The dramatic increase in Bitcoin’s value has drawn the attention of both cybercriminals aiming to exploit system flaws for profit and researchers working to identify these vulnerabilities, devise protective measures, and anticipate future trends. It outlines the Bitcoin protocol by describing its main components, their functions, and how they interact. Moreover, it explores the foundational cryptographic concepts and existing weaknesses within the Bitcoin infrastructure and concludes by assessing the strength and effectiveness of current security approaches.
Authors - Faycal Fedouaki, Mouhsene Fri, Kaoutar Douaioui, Ayoub El Khairi Abstract - The merging of blockchain and the industrial internet of things (IIoT) will reshape how smart manufacturing systems operate. This paper proposes a conceptual framework for using blockchain's decentralization architecture, cryptographic integrity, and smart contract automation to improve process monitoring in industrial environments. With real-time data collection from IIoT devices and secure transparent Blockchain ledgers, the proposed model addresses important issues such as tampering, interoperability, and latency when it comes to decision making. It also supports real-time analytics through incorporating reduced latencies using edge processing and message queuing. Additional design principles will address scalability issues by layered Blockchain structures and fog computing nodes, allowing the framework to keep pace with rising data volumes and increasing device densities. Even though the model is built on the latest breakthroughs and conforms to the Industry 4.0 paradigms, a prototype and experimental simulations are planned to value its empirical viability. Notably, this work intends to establish a resilient and efficient digital infrastructure for Next Generation industrial process monitoring.
Authors - Davide Paglia, Lorenzo Rigatti, Andrea Sabatini, Fabrizio Venettoni Abstract - In the context of the new emerging trend in capital markets of tokenization of financial assets, the paper explains how financial security can be registered on a market blockchain settled in ECB central bank digital currency, in T+0 time and in compliance with the current European regulatory framework. A particular use case is explored through the design, implementation and use of a new market infrastructure DLT based, an enterprise application called DLT Bond Platform for the issuance of a digital bond settled in European Central Bank wholesale digital currency via a delivery versus payment process, using a layer 2 permissionless blockchain. After introducing the context and the problem statement in the first two sections, a general description of the solution proposed, and its novel contributions are provided in the third section. In the forth section the main components of the DLT Bond Platform are described in detail, both web2 and web3 as well as the related business processes, namely: (i) Management of the entire life cycle of a bond in digital form; (ii) Management of all the settlement phases envisaged by the bond also through atomic transactions for the simultaneous transfer of the securities and the corresponding cash flows (delivery versus payment or "DvP") through the use of the solution made available by the Bank of Italy as part of the European Central Bank initiative called New Technologies for Wholesale Settlement (iii) Identification, authorization and management of users, profiles and the respective roles on chain; (iv) real-time monitoring and audit trail. The final section focuses on the results obtained and on the completion of the validation process, ultimately dwelling on potential future developments.
Authors - Abdulrahman Azab, Paul De Geest, Sanjay K. Srikakulam, Tomas Vondrak, Mira Kuntz, Bjorn Gruning Abstract - Effective resource scheduling is critical in high-performance (HPC) and high-throughput computing (HTC) environments, where traditional scheduling systems struggle with resource contention, data locality, and fault tolerance. Meta-scheduling, which abstracts multiple schedulers for unified job allocation, addresses these challenges. Galaxy, a widely used platform for data-intensive computational analysis, employs the Total Perspective Vortex (TPV) system for resource scheduling. With over 550,000 users, Galaxy aims to optimize scheduling efficiency in large-scale environments. While TPV offers flexibility, its decision-making can be enhanced by incorporating real-time resource availability and job status. This paper introduces the TPV Broker, a meta-scheduling framework that integrates real-time resource data to enable dynamic, data-aware scheduling. TPV Broker enhances scalability, resource utilization, and scheduling efficiency in Galaxy, offering potential for further improvements in distributed computing environments.
Authors - Nina Valchkova, Vasil Tsvetkov Abstract - This paper investigates the dynamic characteristics of a collaborative robotic mobile platform with enhanced manipulability. It`s motion parameters, such as linear velocities and accelerations, and their influence on platform control are analyzed. The experiments performed include monitoring acceleration processes, constant lateral movement, and deceleration and braking phases. The presented graphical analyses demonstrate key features of the platform dynamics that can be used to optimize the control of a collaborative robotic mobile platform.
Authors - Sakshi Tiwari, Snigdha Bisht, Kanchan Sharma Abstract - Effective waste management is critical to achieving sustainability in urban regions like Delhi-NCR, where heterogeneous waste streams pose a classification challenge. In this research, we propose WasteIQNet, an intelligent deep hybrid model designed for precise waste classification across 18 categories under a well-defined hierarchy: Wet (Compostable, Special_Disposal) and Dry (Recycle, Reduce, Reuse). Leveraging the WEDR dataset, we first standardized over 1.75 lakh images via JPEG conversion, 256×256 resizing, and RGB formatting. SMOTE+ENN was applied to balance class distributions to 20,000 images each. Feature extraction was achieved through simulated DASC-like global vector embeddings using MobileNetV3. Our baseline hybrid model integrated MobileNetV3Large and GraphSAGE, achieving an initial accuracy of 80.56%. After optimizing the model for multi-label learning through sigmoid activation, threshold-based decoding, and hierarchical label interpretation, we conducted extensive enhancements. Hyperparameter tuning with Optuna, Feature-wise Attention (FWA), and Top-K Mixture of Experts (TopK-MoE) improved accuracy to 83.33%. Subsequent normalization and activation function experiments (Mish, Swish, GELU) led to a peak accuracy of 94.44% using GELU. We further introduced Dynamic Sparse Training (DST) and Model-Agnostic Meta-Learning (MAML), raising accuracy to 95.04%. Final enhancements included label smoothing and early stopping, culminating in a best-in-class accuracy of 97.87%. WasteIQNet demonstrates a scalable, interpretable, and high-performance solution for automated waste classification, supporting smart city initiatives and responsible environmental management.
