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Articles 3811 - 3840 of 291657

Full-Text Articles in Physical Sciences and Mathematics

Gc-161-210 Hybrid Path Planning Using Genetic Algorithm, Caitlin Tigani, Ramisa Fariha Joyee, Wasif Mohammad Apr 2026

Gc-161-210 Hybrid Path Planning Using Genetic Algorithm, Caitlin Tigani, Ramisa Fariha Joyee, Wasif Mohammad

C-Day Computing Showcase

This research investigates whether uninformed search (BFS) or informed search (A*) is more effective when combined with a Genetic Algorithm for maze path planning. We design and implement four algorithms: baseline BFS and A*, hybrid GA+A*, and hybrid GA(BFS+A*). Our findings show that while A* alone performs optimally, integrating it with GA can produce alternative quality solutions, though with computational trade-offs. The study demonstrates that GA+A* provides the best balance between solution quality and runtime efficiency.

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Gc-178-191 Communication App: Ai-Assisted Aac Platform​, Alex Wills, Maryam Koya Apr 2026

Gc-178-191 Communication App: Ai-Assisted Aac Platform​, Alex Wills, Maryam Koya

C-Day Computing Showcase

The Communication App is an accessibility-focused mobile application designed to support individuals with speech impairments, strong or unrecognized accents, and neurodivergent communication needs. The system leverages AI assisted speech-to-text (STT) and text-to-speech (TTS) technologies to enable real-time and seamless communication between users.This project aims to bridge communication gaps by providing a customizable, adaptive platform that learns user speech patterns over time. The application integrates cloud-based services, secure communication protocols, and an intuitive user interface to ensure usability, performance, and accessibility.

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Grm-010-170 Ai-Enabled Water Quality Framework For E. Coli Prediction And Forecasting, Sangeetha Devaraj, Jui Mhatre Apr 2026

Grm-010-170 Ai-Enabled Water Quality Framework For E. Coli Prediction And Forecasting, Sangeetha Devaraj, Jui Mhatre

C-Day Computing Showcase

Water quality monitoring is essential for public health and environmental sustainability, yet existing monitoring infrastructures remain sparse, fragmented, and incomplete. Data from the United States Geological Survey (USGS) indicate that while over 1.5 million sites are cataloged in the USGS Water Data for the Nation, only a small fraction are actively reporting water quality measurements, with significant reductions observed in recent years. Moreover, critical parameters such as pH, water temperature, dissolved oxygen, turbidity, and microbial indicators like Escherichia coli are inconsistently measured, with widespread missing and irregular data. This work presents an AI-enabled water quality data framework designed to address …

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Grm-012-173 Can You Trust Ai Code? Understanding And Detecting Breaking Changes Using Llms, K M Ferdous, Kowshik Chowdhury Apr 2026

Grm-012-173 Can You Trust Ai Code? Understanding And Detecting Breaking Changes Using Llms, K M Ferdous, Kowshik Chowdhury

C-Day Computing Showcase

AI-generated code is increasingly prevalent in software engineering practices, yet its reliability in preserving backward compatibility remains underexplored. This paper presents a unified study of (i) how often AI-generated code introduces breaking changes and (ii) whether large language models (LLMs) can detect such changes from commit-level diffs with explanations. We analyze 7,191 agent-generated and 1,402 human-authored pull requests from Python repositories using an AST-based approach to identify potential breaking changes. Our results show that AI agents introduce fewer breaking changes overall than humans (3.45% vs. 7.40%) in code generation tasks. However, agents show higher risk in maintenance tasks, where refactoring …

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Grm-081-207 Leveraging Non-Parametric Longitudinal Rank Sum Tests (Lrst) For Robust Global Treatment Effect Estimation In Alzheimer’S Disease, Imaan Shahid Apr 2026

Grm-081-207 Leveraging Non-Parametric Longitudinal Rank Sum Tests (Lrst) For Robust Global Treatment Effect Estimation In Alzheimer’S Disease, Imaan Shahid

C-Day Computing Showcase

Parametric approaches, such as Mixed Models for Repeated Measures (MMRM), are standard in Alzheimer’s Disease (AD) clinical trials. However, these models often falter when data violates assumptions of normality or follows non-linear trajectories—common occurrences in AD due to floor/ceiling effects on cognitive scales and heterogeneous disease progression. This study evaluates Longitudinal Rank Sum Tests (LRST) as a non-parametric alternative to maintain statistical power and robustness.

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Grm-094-176 Influence Of Speech Disfluencies And Prompt Optimization On Llm-Based Alzheimer's Detection, Muhammad Awais Arshad Apr 2026

Grm-094-176 Influence Of Speech Disfluencies And Prompt Optimization On Llm-Based Alzheimer's Detection, Muhammad Awais Arshad

C-Day Computing Showcase

This study evaluates how speech disfluencies and prompting strategies impact LLM-based Alzheimer’s Disease (AD) detection. We compared transcripts with preserved disfluencies (ADReSS) against clean transcripts (ADReSSo) using four state-of-the-art LLMs. Key Discovery: Complex prompts induce a "mirror-image" classification bias, where DeepSeek models severely over-classify AD, and GPT-5.2 over-classifies Cognitively Normal (CN) individuals. Optimization Fix: Applying DSPy MIPROv2 effectively mitigated bias in simpler prompts, while TextGrad successfully optimized complex, multi-step prompts.

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Grm-095-230 A Multimodal Llm Framework For Automated Construction Blueprint Analysis With Real-Time Decision Support, Muhammad Awais Arshad Apr 2026

Grm-095-230 A Multimodal Llm Framework For Automated Construction Blueprint Analysis With Real-Time Decision Support, Muhammad Awais Arshad

C-Day Computing Showcase

This study addresses the high hallucination rates of Vision-Language Models (LLMs) when analyzing complex, hybrid construction blueprints. We developed a dual-input pipeline that pairs high-resolution images with a four-layer JSON "Digital Twin" (vector text, raster OCR, geometry) to mathematically ground the LLM's visual interpretation. Key Engineering Achievement: We processed a massive 137-sheet civil engineering project with zero errors. By introducing a multi-tier JSON pruning strategy, we cut token usage by up to 70% and processed the entire batch from $13-$15 to just $0.93. Decision Support Extension: We integrated real-time traffic data (TomTom) and GDOT procedural policies to transform the pipeline …

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Grm-132-159 Integrating Causal Inference With Graph Neural Networks For Alzheimer’S Disease Analysis, Pranay Kumar Peddi Apr 2026

Grm-132-159 Integrating Causal Inference With Graph Neural Networks For Alzheimer’S Disease Analysis, Pranay Kumar Peddi

C-Day Computing Showcase

Deep graph learning has advanced Alzheimer’s disease (AD) classification from MRI, but most models remain correlational, confounding demographic and genetic factors with disease-specific features. We present Causal-GCN, an interventional graph convolutional framework that integrates do-calculus-based back-door adjustment to identify brain regions exerting stable causal influence on AD progression. Each subject’s MRI is represented as a structural connectome where nodes denote cortical and subcortical regions and edges encode anatomical connectivity. Confounders such as age, sex, and APOE4 genotype are summarized via principal components and included in the causal adjustment set. After training, interventions on individual regions are simulated by severing their …

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Grm-159-214 Transportation Energy And Emission Modeling And Analysis Tool (Teemat), Laeticia Neno Aloyem Apr 2026

Grm-159-214 Transportation Energy And Emission Modeling And Analysis Tool (Teemat), Laeticia Neno Aloyem

C-Day Computing Showcase

The Transportation Energy and Emission Modeling and Analysis Tool (TEEMAT) is a web-based decision-support framework for evaluating the environmental impacts of EV adoption across U.S cities. TEEMAT integrates a feedforward neural network trained on MOVES 4.0 for tract-level vehicle emissions (CO₂, NOₓ, PM₂.₅), a macroscopic traffic and activity-based model capturing congestion-driven emission spikes, and a Meta-Prophet model trained on NREL Cambium data for grid emissions (CO₂, CH₄, N₂O). Results show that while EV adoption reduces tailpipe emissions, rising travel demand and congestion-induced low speeds can significantly offset these gains underscoring that meaningful decarbonization requires coordinated transportation and energy grid strategies.

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Grm-169-137 Predicting The Stock Market's Next Move: How Neural Network Architecture Shapes Forecasting Accuracy, Roderick Powell Apr 2026

Grm-169-137 Predicting The Stock Market's Next Move: How Neural Network Architecture Shapes Forecasting Accuracy, Roderick Powell

C-Day Computing Showcase

Three feedforward neural network (FFNN) architectures — bottleneck, parallel multi-path, and residual parallel — were trained on ten years of daily S&P 500 (SPY ETF) price and volume data to predict next-day market direction (Up/Down). All three demonstrated predictive ability above random chance. Architectural choice directly determined class prediction bias: the bottleneck concentrated errors on Up days, the parallel architecture distributed them evenly, and residual connections inverted the bias toward Down days. Model 2 (parallel) achieved the highest test accuracy (58.2%) and the most balanced class predictions among the three configurations.

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Grm-170-221 Mind Modeling For Neuroadaptive Vr Labs, Harish Chary Nagulapally Apr 2026

Grm-170-221 Mind Modeling For Neuroadaptive Vr Labs, Harish Chary Nagulapally

C-Day Computing Showcase

Students with motor impairments lack equal access to hands-on STEM laboratory experiences. This study investigates whether EEG signals and eye-gaze data contain distinguishable patterns linked to specific hand motor functions both physical and imagined. Identifying this relationship is the critical first step toward a BCI-driven VR system for accessible STEM education.

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Grm-174-228 Mitigating Prompt-Induced Variability In Llm Outputs, Crystal Tubbs Apr 2026

Grm-174-228 Mitigating Prompt-Induced Variability In Llm Outputs, Crystal Tubbs

C-Day Computing Showcase

Large language models in enterprise settings often produce structurally invalid outputs when users communicate informally, creating silent failure modes that pass unnoticed in downstream systems. This study investigates how prompt variation alone impacts schema compliance and output reliability. We evaluate three architectures across two tasks and four prompt styles, isolating the effect of interaction style on model behavior. Results show that baseline systems achieve 100% compliance under structured prompts but fail completely under ambiguous and casual inputs. A minimal reliability pipeline consisting of generation, self critique, and schema validation restores 100% compliance across all conditions at a predictable computational cost.

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Grm-176-225 Memoryeil: An Enhanced Memory Layer Architecture For Heterogeneous Robots, Yukang Shen Apr 2026

Grm-176-225 Memoryeil: An Enhanced Memory Layer Architecture For Heterogeneous Robots, Yukang Shen

C-Day Computing Showcase

Embodied agents still struggle to generalize across robot types, tasks, and environments because most policies remain tightly tied to robot-specific observations and action spaces. While recent VLA and planning methods improve task performance, they still lack a shared memory layer for storing and reusing experience across heterogeneous robotic systems. We propose MemoryEIL, a predicate-based memory layer that converts multimodal observations and execution traces into structured graph memories while preserving raw embeddings for fine-grained retrieval and disambiguation. MemoryEIL separates short-term task belief from long-term experience and plugs retrieved memories into either VLA policies or differentiable TAMP planners. Preliminary results show better …

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Grp-01-196 Topological Drift Predicts Epidemic Instability, Charles Fanning Apr 2026

Grp-01-196 Topological Drift Predicts Epidemic Instability, Charles Fanning

C-Day Computing Showcase

We study whether changes in contact-network topology predict transitions into high-risk epidemic periods across several classical empirical proximity network datasets. We use temporal graph learning with persistent homology-based topological signals and evaluate large-outbreak risk using SIR simulations to test whether topological drift serves as an early warning signal for epidemic instability.

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Grp-03-141 Stress-Testing Parkinson’S Disease Screening: A Cross-Modal Analysis Of Drawing And Speech Models, Rehma Razzak Apr 2026

Grp-03-141 Stress-Testing Parkinson’S Disease Screening: A Cross-Modal Analysis Of Drawing And Speech Models, Rehma Razzak

C-Day Computing Showcase

Medical AI systems are increasingly deployed in clinical settings, yet most published models report only clean accuracy, dataset details, and training procedures—while omitting security‑critical evaluations such as robustness to perturbations, adversarial vulnerability, and failure modes under realistic noise. This project addresses that gap by building a cross‑modal robustness assessment for Parkinson’s disease (PD) screening models across handwriting trajectories, speech‑derived acoustic features, and an LLM‑based preprocessing layer. Despite strong clean performance (visual subject‑level ROC AUC ≈ 0.99; audio ≈ 1.0), the visual pipeline proved highly brittle to realistic acquisition distortions. Downsampling and point‑dropout caused near‑chance collapse, while pressure noise and XY …

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Grp-08-168 Diagnosing Faults In Electrical Power Systems Of Satellites, Jared Lasley, Nguyen Thi Binh Nguyen Apr 2026

Grp-08-168 Diagnosing Faults In Electrical Power Systems Of Satellites, Jared Lasley, Nguyen Thi Binh Nguyen

C-Day Computing Showcase

Satellite systems cost hundreds of millions of dollars or more to launch. To be resistant to catastrophic failures (and total loss of investment), satellite systems are designed with redundant sub-systems and are further equipped with numerous sensors and other health-monitoring sub-systems. In this poster, we consider an approach to fault diagnosis based on probabilistic logic programming. In particular, we propose to use ProbLog to model and reason with the electrical power system (EPS) of a satellite. Once we model a system using (probabilistic) first-order logic, we can take the system state and any (unexpected) sensor readings, and through automated reasoning, …

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Grp-096-183 Early Warning Signals For Geopolitical Oil Shocks Via Multi-Model Nlp Sentiment Analysis, Nzubechukwu Ohalete Apr 2026

Grp-096-183 Early Warning Signals For Geopolitical Oil Shocks Via Multi-Model Nlp Sentiment Analysis, Nzubechukwu Ohalete

C-Day Computing Showcase

The Strait of Hormuz carries roughly 20% of the world’s daily oil supply. Its closure on March 4, 2026, sent Brent crude surging 36.6%, from $74.64 to a peak of $118.35/barrel. Traditional time-series models fail during such unprecedented shocks because the historical price data contains no analog. This study evaluates whether NLP sentiment analysis can detect crisis signals in news text before they appear in prices, and whether agreement patterns across models predict volatility. We score 2,249 Guardian news articles using five deterministic sentiment models across three tiers: a lexicon baseline (VADER), a general-purpose transformer (RoBERTa-CardiffNLP), and three financial-domain transformers …

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Grp-114-127 Gamma-Sieve: Structural De-Obfuscation Of Financial Regime Manipulation Via Heterophilic Graph Neural Networks, Christopher Regan Apr 2026

Grp-114-127 Gamma-Sieve: Structural De-Obfuscation Of Financial Regime Manipulation Via Heterophilic Graph Neural Networks, Christopher Regan

C-Day Computing Showcase

Market manipulation increasingly exploits fragmentation — dispersing orders across dozens of accounts, venues, and sub-second timing windows — to evade rule-based surveillance. We present Gamma-Sieve, a heterophilic graph neural network approach that constructs heterogeneous transaction graphs (four node types, ten edge types) from market microstructure data, applying CARE-GNN with RL-gated edge filtering and TFE-GNN with spectral triple-frequency decomposition. At production scale, heterophilic GNNs outperform a bidirectional LSTM baseline by +16% AUC on fragmented coordination attacks. However, evaluation on real NASDAQ equity data (LOBSTER Level 3) reveals a critical domain-shift challenge: GNN false positive rates of 42–88% on legitimate trading, caused …

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Grp-146-136 Platonic Policy Representations: Navigating Learned Manifolds For Rapid Adaptation, Cameron Redovian Apr 2026

Grp-146-136 Platonic Policy Representations: Navigating Learned Manifolds For Rapid Adaptation, Cameron Redovian

C-Day Computing Showcase

Adapting reinforcement learning policies to changing dynamics is typically addressed by domain randomization, which trains a single robust policy at the cost of specialization, or by meta-RL methods, which enable rapid adaptation but require online inference or optimization. We propose a different mechanism: extending the Platonic Representation Hypothesis (Huh et al., 2024) and vec2vec (Jha et al., 2025) to policy space, we show that diverse task-competent policies trained under varying dynamics admit a shared, low-dimensional manifold structure that is learnable from trajectory embeddings. Platonic Policy Representations (PPR) learns this manifold via geometric preservation losses, then navigates it for rapid adaptation: …

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Grp-150-192 From Construction Floor-Plan To Robot-Ready Navigation Map: A Dual-Memory Multi-Agentic Ai That Learns From Its Own Successes And Failures, Amatul Akhi Apr 2026

Grp-150-192 From Construction Floor-Plan To Robot-Ready Navigation Map: A Dual-Memory Multi-Agentic Ai That Learns From Its Own Successes And Failures, Amatul Akhi

C-Day Computing Showcase

Construction floor plans contain rich architectural information, but they are not directly usable for robotic navigation in IoT-enabled smart buildings. They often require manual processing to remove irrelevant annotations and extract navigable layouts. Existing methods either depend on fixed image-processing pipelines that do not generalize well across different floor plan styles or on data-intensive learning models that require large annotated datasets. We propose a dual-memory multi-agent framework that treats floor plan-to-map conversion as a sequential, experience-driven decision process under data scarcity. The framework uses three cooperative agents for perception, decision-making, and evaluation, which interact through a shared persistent memory represented …

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Grp-163-229 Emotion Elicitation Via Empathic Ai (E-Ai) Agent Teacher In Vr Classroom, Arpita Paul Apr 2026

Grp-163-229 Emotion Elicitation Via Empathic Ai (E-Ai) Agent Teacher In Vr Classroom, Arpita Paul

C-Day Computing Showcase

How emotions influence learning experiences in virtual reality (VR) remains unclear. To address this gap, we investigated the impact of emotionally elicited lectures on learners’ experiences and outcomes in a pedagogical VR classroom using a between-subjects design with two emotional conditions (positive vs. negative). Emotional cues were delivered by a virtual agent (VA) teacher through bodily gestures and verbal expressions during lecture delivery. In a user study (N=34), we collected multimodal data, including neurophysiological measures such as electroencephalography (EEG), galvanic skin response (GSR), heart rate (HR), heart rate variability (HRV), skin temperature, and eye gaze, along with self-reported emotion, learning …

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Grp-180-147 Quantum Augmented Microgrids (Quam) Simulator, Nitin Jha, Prateek Paudel Apr 2026

Grp-180-147 Quantum Augmented Microgrids (Quam) Simulator, Nitin Jha, Prateek Paudel

C-Day Computing Showcase

Small modular nuclear reactors (SMRs) are redefining the energy generation landscape by enabling the deployment of modular, scalable, and pre-built power units that can be used to build distributed autonomous microgrids for critical infrastructure and burgeoning AI factories. Often, these microgrids are linked together to provide a resilient, decentralized power generation infrastructure. Consequently, the cybersecurity of microgrids is of critical importance. In this work, we propose a quantum augmented network framework for resilient microgrids. We integrate the ideas of secure quantum networking, quantum anonymous notification, and quantum random number generation to strengthen the integrity, confidentiality, and privacy of microgrid networks. …

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Uc-011-171 Beyond Postseason Awards: Predicting Accolades Via Real-Time Control Chart Signals In The Ncaa Transfer Era, Daniel Bowen, Shayaan Cyclewalla Apr 2026

Uc-011-171 Beyond Postseason Awards: Predicting Accolades Via Real-Time Control Chart Signals In The Ncaa Transfer Era, Daniel Bowen, Shayaan Cyclewalla

C-Day Computing Showcase

In the era of the NCAA transfer portal, collegiate basketball coaches face the critical challenge of identifying and targeting elite recruits within a condensed 15-day window. This study investigates the predictability of elite player performance by analyzing postseason award winners within the Coastal Athletic Association (CAA). Utilizing game-by-game data on player efficiency, usage percentages, and Player Efficiency Ratings (PER), we implemented an Exponentially Weighted Moving Average (EWMA) control chart—a technique from the Statistical Process Control (SPC) family—to monitor performance signals. Our results indicate that the EWMA model successfully identifies future award-winning players after an average of only 8.58 games. By …

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Uc-082-216 Ai Driven Guest Support For Vacationsforyou​, Cassidie Grogan, Kendal Elison, Benjamin Dulcio, Ezra Begashaw, Wilfred Faltz Apr 2026

Uc-082-216 Ai Driven Guest Support For Vacationsforyou​, Cassidie Grogan, Kendal Elison, Benjamin Dulcio, Ezra Begashaw, Wilfred Faltz

C-Day Computing Showcase

The AI Guest Support Assistant is a proof-of-concept, web-based chatbot designed to streamline guest support for a high-volume vacation rental operation. The system leverages a Large Language Model (LLM) combined with a Retrieval-Augmented Generation (RAG) approach to deliver accurate, context-aware responses to common guest inquiries, such as reservation details and check-in times. Built using a React frontend and a FastAPI backend, the platform integrates securely with the StreamlineVRS property management system. The solution aims to reduce call center workload by automating repetitive inquiries while maintaining a clear escalation path for more complex requests. This project evaluates the feasibility, usability, and …

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Uc-086-235 Student Performance Pattern Mining, Cesar Arevalo Colocho Apr 2026

Uc-086-235 Student Performance Pattern Mining, Cesar Arevalo Colocho

C-Day Computing Showcase

This project applies data mining techniques to explore patterns in a student performance dataset. The analysis focuses on discovering natural groupings of students and frequent associations among academic, social, and lifestyle attributes. Clustering and association rule mining are used to identify meaningful structures in the data, emphasizing pattern discovery and interpretation rather than outcome prediction.

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Uc-087-236 Early Prediction Of Player Performance, Grady Freeman, Hien Truong, Jackson Mayo Apr 2026

Uc-087-236 Early Prediction Of Player Performance, Grady Freeman, Hien Truong, Jackson Mayo

C-Day Computing Showcase

This study examines whether early-season performance metrics can support player evaluation under the NCAA’s shortened transfer window. Using data from Conference USA and the Mid-American Conference, we modeled offensive (UASE) and defensive (DAR) efficiency with multiple predictive methods. Across both full-season and 9-game datasets, DAR was more predictable, with higher R² and lower RMSE values. Linear Regression consistently performed best for DAR, while KNN and Random Forest performed best for UASE depending on the dataset. Results show that meaningful performance patterns can be identified early in the season, even with limited data. These findings suggest analytics can help programs make …

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Uc-098-187 Zero-Inflated Poisson Modeling Of Ncaa Postseason Awards, Charles Lane, Kyle Bresko, Kaleb Treang Apr 2026

Uc-098-187 Zero-Inflated Poisson Modeling Of Ncaa Postseason Awards, Charles Lane, Kyle Bresko, Kaleb Treang

C-Day Computing Showcase

Our project focuses on predicting postseason awards for NCAA Men's College Basketball which can be difficult to model given that less than 15% of players in a given season win awards. After evaluating basic models, we selected a Zero-Inflated Poisson (ZIP) model to account for most players receiving zero awards. We identified free-throw attempts as being the best predictor for the structural zeros present in who can win an award. The final ZIP model produced better evaluation metrics than other basic models. Accounting for structural zeros allowed us to better model how on court statistics can translate into postseason awards.

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Uc-099-189 Spectre, Alexander Tobal, Chris Higgins Jr, Jaylin Reeves, Logan Leichter Apr 2026

Uc-099-189 Spectre, Alexander Tobal, Chris Higgins Jr, Jaylin Reeves, Logan Leichter

C-Day Computing Showcase

Spectre consists of four levels, where players complete various objectives and fight off ghosts while doing so. Our tutorial level introduces players to the mechanics, such as shooting, rear view mirror shooting, walking and jumping. With the rest of the levels focusing on completing objectives in order to progress. The final level culminates in a boss fight, ending the journey. While players explore and complete objectives, enemies drop a currency that players can spend to obtain upgrades. Getting hit by enemies not only reduces the players’ health but also applies debuffs to them making players more cautious of their surroundings. …

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Uc-117-213 Haunted Owl Hotel – A 3d Horror Maze Chase Game, Carter Griffin, Rin Egl, Kcyana Redmon, Jose Portillo, Alana Nesbit Apr 2026

Uc-117-213 Haunted Owl Hotel – A 3d Horror Maze Chase Game, Carter Griffin, Rin Egl, Kcyana Redmon, Jose Portillo, Alana Nesbit

C-Day Computing Showcase

“Haunted Owl Hotel” is a horror Pac-Man-inspired, 3D maze chase game. You play as a cute owl named Sappy trying to escape the scary hotel, but suddenly your elevator breaks down. Navigate the spooky halls to collect the candles left behind on each floor to reactivate the elevator, but be careful, after grabbing each candle, the darkness left behind will follow you. Ghosts lurk around every corner hoping to make you their next victim. Descend through each floor without losing all 3 lives to escape the haunted owl hotel and win the game.

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Uc-121-133 Head In The Clouds, Hunter Osborne, Jane Day, Chase Bell Apr 2026

Uc-121-133 Head In The Clouds, Hunter Osborne, Jane Day, Chase Bell

C-Day Computing Showcase

Head in the Clouds is a video game that puts the player in the shoes of a child with ADHD (Attention Deficit Hyperactivity Disorder). Rain, the protagonist, is told by their mother to take out the trash, but keeps getting distracted and daydreaming instead. The player must beat platforming challenges to get Rain back on task. The narrative and gameplay is meant to represent the difficulties of having ADHD.

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