Database Scientist

Secure and Efficient Medical Record Sharing for Pandemic Response Using ChainMaker Blockchain Introduction to the Problem Space Large-scale pandemics like COVID-19 demand centralized patient treatment and rapid data sharing. However, conventional systems fail to balance performance, privacy, and efficiency. Patient medical records shared during emergencies are often vulnerable to breaches, raising ethical and regulatory concerns. Technological Foundations of the Proposed System ChainMaker offers high throughput (up to 240M TPS) and post-quantum security, ideal for high-load medical applications. IPFS supports decentralized storage with resilience and reliability. TBFT ensures strong consensus without PoW, while SM2 adds ECC-based, government-backed security for data and identity protection. Key Cryptographic Mechanisms C-PRE enables re-encryption under preset conditions (e.g., time-based access), allowing patients to define when and how their records are shared. ASE lets encrypted dat...

AI-Powered Critical Care Analytics: Database Deployment and Query Generation via ICU-GPT 🔹 Introduction ICU-GPT is an AI-driven platform designed to simplify the deployment, visualization, and data extraction from critical care medical databases such as MIMIC-III, MIMIC-IV, and eICU-CRD. It leverages Large Language Models (LLMs) like GPT to empower clinicians to interact with complex datasets using natural language, eliminating the need for in-depth programming or database querying skills. . 🔹 Motivation and Background Critical care units generate massive volumes of heterogeneous data—ranging from lab values and imaging to waveforms and clinical notes. Despite the presence of high-quality public databases, data analysis remains a challenge for clinicians due to the technical skills required for SQL querying and data structuring. ICU-GPT addresses this gap through an intuitive, natural language-based interface backed by LLMs. 🔹 Platform Architecture Overview ICU-GPT’s system archite...

Spatio-Temporal Deep Learning for Predicting Signal Plan Execution in Urban Traffic Networks  1. Introduction to Intelligent Traffic Signal Control Modern cities face increasing traffic congestion due to urbanization and limited road infrastructure. Traditional traffic signal systems, often manually configured or based on fixed time cycles, struggle to adapt to real-time conditions. To address these limitations, this research explores the use of deep learning techniques—specifically Spatio-Temporal Graph Convolutional Networks (STGCNs)—to develop a responsive, automated traffic signal management system that adjusts signal timings dynamically based on real-time traffic flow. 2. Role of Graph Neural Networks in Traffic Modeling Urban road networks can be naturally represented as graphs, where intersections are nodes and roads are edges. Graph Neural Networks (GNNs), particularly Graph Convolutional Networks (GCNs), are powerful tools for analyzing such graph-structured data. GCNs ag...

  Best Researcher Awards Introduction The Best Researcher Awards serve as a prestigious recognition of excellence in academic and scientific research. These awards are designed to honor individuals who have made significant contributions to their respective fields through rigorous, original, and impactful research. Whether in science, technology, medicine, or the humanities, these awards highlight the dedication and perseverance of researchers who advance knowledge and inspire future generations. Purpose and Significance The primary purpose of the Best Researcher Awards is to acknowledge outstanding research efforts and motivate continued innovation. These awards not only recognize individual achievements but also promote a culture of inquiry and excellence within academic and professional communities. Celebrating research excellence encourages knowledge sharing, raises public awareness about the value of research, and fosters collaboration across disciplines and borders. Eligibil...

Designing Robust Runtime Activity Detection Services from IoT Streams: A Smart Manufacturing and Healthcare Perspective 1. Introduction The rapid adoption of Internet of Things (IoT) technologies has led to an explosion of sensor data capturing real-world activity executions in domains such as manufacturing and healthcare. While traditional Business Process Management (BPM) relies on centralized systems (PAIS) to log and monitor activities, many IoT environments lack such systems. This creates challenges in applying process mining and analytics directly to IoT-generated data due to its low abstraction level. 2. Problem Statement IoT data is often granular, raw, and not immediately suitable for process mining. Existing approaches rely on supervised machine learning, which are expensive to train, require historical data, and only support post-mortem analysis. There's a need for real-time , sensor-agnostic , and low-code or no-code solutions that enable online process analyt...

Data-Driven Prediction of HT-ATES Temperature Profiles Using Machine Learning and Nearest Neighbor Search 1. Introduction Climate Challenge & Heating Sector : Heating accounts for ~40% of global energy demand and is a major contributor to CO₂ emissions. Role of HT-ATES : High-Temperature Aquifer Thermal Energy Storage (HT-ATES) can reduce fossil fuel use by storing excess heat for use during peak demand. Need for Efficient Modeling : Traditional numerical models (FEM, FDM, FVM) are accurate but computationally intensive (1–10 hours per run), making large-scale integration impractical. 2. Modeling Approaches for HT-ATES 2.1. Numerical Modeling Finite difference, finite volume, and finite element methods simulate fluid and heat flow accurately. Require complex setups and significant runtime. Unsuitable for real-time or large-scale energy system planning. 2.2. Analytical Modeling Only one known analytical method exists. Fast but inaccurate across wide param...