Phenomenological Research Awards

Improving CPP Prediction with Hybrid Feature Integration and Ensemble Machine Learning Background & Motivation Cell-penetrating peptides (CPPs) are short peptides capable of traversing biological membranes, playing a transformative role in modern drug delivery systems. Their ability to facilitate the intracellular transport of therapeutic agents—including nucleic acids, proteins, and small molecules—makes them key candidates in developing targeted therapies for conditions like cancer, genetic disorders, and neurodegenerative diseases. However, experimental identification of novel CPPs is resource-intensive, slow, and impractical for large-scale screening of potential sequences. Limitations in Existing Methods While existing computational methods have made progress in CPP prediction using either: Conventional features (such as amino acid composition, charge, and hydrophobicity), or Protein Language Models (PLMs) (deep learning-based models trained on large-scale protein da...

Integrated Optimization and Dynamic Pricing for PV-EV Charging Stations Using Hybrid Energy Storage Under Temperature Variability 1. Introduction With the growing adoption of electric vehicles (EVs), the need for efficient, sustainable, and grid-friendly charging infrastructure has become paramount. Integrating renewable energy sources, such as photovoltaic (PV) systems, and deploying energy storage solutions has shown significant promise. However, environmental factors—especially temperature—greatly influence system performance, including PV efficiency, battery capacity, and EV charging demand. This study proposes a comprehensive optimization framework that incorporates temperature variations into dynamic pricing and charging strategies for enhanced operational efficiency. 2. System Architecture Overview The proposed system includes a PV-powered EV charging station integrated with a hybrid energy storage setup composed of supercapacitors and lithium-ion batteries . A coordinated co...

High-Performance Approximate Squaring Circuits with Recursive Design and Double-Sided Error Compensation 1. Introduction In the era of compact, high-performance, and energy-efficient digital systems, traditional arithmetic circuits face challenges due to their high resource requirements. Squaring operations, which are commonly used in signal processing, image analysis, and machine learning, often consume significant area and power. This work explores a hardware-efficient approach using approximate computing and recursive squaring techniques to reduce complexity while maintaining acceptable levels of computational accuracy. 2. Approximate Computing in Arithmetic Circuits Approximate computing is a design strategy that allows small, controlled inaccuracies in arithmetic operations to reduce hardware overhead. This paradigm is well-suited for error-resilient applications like multimedia processing, where perfect precision is not critical. By leveraging approximate logic, designers can ...

  🔬 Spectral Distance: A New Era in Biosimilar Structural Comparison 📊 In the world of biologics, ensuring that biosimilar products match their reference counterparts is critical for both patient safety and product efficacy. While biosimilars are designed to be "highly similar" to existing biologics, evaluating their higher-order structures (HOS) poses a unique scientific challenge. One powerful technique used to probe these complex structures is circular dichroism (CD) spectroscopy . However, many assessments of CD spectra have historically relied on visual comparison alone 👀 — a method that, while intuitive, lacks objectivity. This article introduces a quantitative, statistical approach to comparing biosimilar and reference products using spectral distance calculations on CD spectra, offering a new level of precision for researchers and technicians 🧪📈. 📘 Understanding the Basics Circular dichroism (CD) spectroscopy is a widely accepted analytical method used to e...

Robust Displacement Estimation in Fast-Moving Structures Using Doppler-Aware mmWave Radar Processing Radars offer high resolution but face challenges such as phase wrapping when tracking fast-moving targets. To address this, the authors propose an iterative phase unwrapping technique that leverages Doppler information to recover accurate displacement data. Experimental results demonstrate the effectiveness of the approach in capturing fine-scale movements even under conditions where traditional radar methods fail. The method is particularly useful for non-contact structural health monitoring, offering low complexity and high precision. Background and Motivation The increasing demand for robust, precise, and non-invasive monitoring tools in structural health monitoring (SHM) has driven the exploration of advanced radar technologies. Among these, millimeter-wave (mmWave) MIMO FMCW radar systems have gained substantial attention due to their ability to provide high-resolution measurement...

  🌫️ Postural Control Modulation in Response to Pollution Perception: A New Window into Environmental Embodiment In our increasingly urbanized and polluted world, understanding how the human body responds to environmental stressors is more important than ever. While most discussions around pollution center on physiological damage or long-term health consequences, recent studies are beginning to highlight a new dimension— how pollution affects our postural control and embodied emotional responses . 🧠🌍 This blog post explores a fascinating experimental study that brings together socioaffective neuroscience, embodied cognition, and ecological psychology . It delves into how our postural system responds to pollution cues and what this might tell us about empathy, environmental engagement, and human-nature interactions. 🎯 The Aim: Merging Postural Science with Environmental Neuroscience The primary goal of the study was to investigate how visual pollution cues affect postural con...