Digital Commons Network^™

Tahap-Tahap Pembinaan Sistem Pendidikan Hukum Dalam Pembangunan Jangka Panjang Kedua (Pjp Ii), Mardjono Reksodiputro

Jurnal Hukum & Pembangunan

Abstract

Exploring The Dual Advising System For Students’ Success: A Developmental Advising Model, Louisa Godwyll, Eugene Kwarteng-Nantwi, Pious Jojo Adu-Akoh

Mid-Western Educational Researcher

This qualitative study explored the dual advising system for student success at a U.S. higher education institution. Research has indicated that approximately 40–60% of students enrolled in doctoral programs do not complete their doctoral degrees. High attrition rates have led some institutions to adopt a dual advising system, a form of developmental advising, to support doctoral students’ success. Creamer and Creamer’s (1994) developmental advising model served as the conceptual framework of the study. The model defines the first stage of advising as defining task, where advisors engage in teaching and utilize strategies to help students attain educational, career, and individual …

Memahami Tindakan Anti-Dumping Masyarakat Eropa (M.E.), Muhammad Ashri

Jurnal Hukum & Pembangunan

Abstract

Prinsip-Prinsip Sistem Perseroan Terbatas Menurut Undang-Undang No.1 Tahun 1995, Muchyar Yara

Jurnal Hukum & Pembangunan

Abstract

Perlukah Undang-Undang Tentang Perlindungan Fakir Miskin Dan Anak-Anak Terlantar?, Yusril Ihza Mahendra

Jurnal Hukum & Pembangunan

Abstract

Izin Kawin Bagi Suami Yang Akan Beristeri Lebih Dari Seorang, Wahyono Darmabrata

Jurnal Hukum & Pembangunan

Abstract

Pembinaan Pendidikan Tlnggi Hukum Dalam Pembangunan Jangka Panjang Kedua (Pjpt Ii), Mardjono Reksodiputro

Jurnal Hukum & Pembangunan

Abstract

Masalah Delik Perzinahan Di Indonesia Dewasa Ini, Topo Santoso

Jurnal Hukum & Pembangunan

Abstract

Kedudukan Dan Peranan Hukum Tata Negara Dalam Pembangunan, Jimly Asshiddiqie

Jurnal Hukum & Pembangunan

Abstract

Pengertian "Keuangan Negara", Harun Alrasyid

Jurnal Hukum & Pembangunan

Abstract

Tanggung Jawab Profesi Penunjang Pasar Modal Di Indonesia, A. Zen Umar Purba

Jurnal Hukum & Pembangunan

Abstract

Penculikan Transnasional Dan Kewenangan Pengadilan Amerika Serikat, D. Sidik Suraputra

Jurnal Hukum & Pembangunan

Abstract

Innovative Approach To Enhance The Efficiency Of Building Integrated Photovoltaic Panels Through Automated Cooling And Cleaning Using Condensate Water In Extreme Climates, Ali Hasan Shah

Thesis/ Dissertation Defenses

Photovoltaic (PV) systems in hot and arid climates experience significant performance degradation due to elevated operating temperatures and rapid dust accumulation. Temperature increase alone reduces power output by about 0.4% to 0.5% per degree Celsius above rated conditions. In desert regions, module temperature commonly exceeds standard conditions, which results in power loss above 10%. Dust accumulation adds further degradation, and field measurements report soiling losses above 1% per day without cleaning, with cumulative losses exceeding 20% within short exposure periods. When high temperatures and dust occur together, the combined effect leads to a severe reduction in energy yield. Current mitigation …

Shelter Portal: Qr-Based Service Tracking For Low-Barrier Shelters, Kate Steele, Colton Knopik, Michael Fischer

2026 Symposium

Shelter Portal is a web-based service tracking application developed for low-barrier shelters, including Catholic Charities’ House of Charity and Rising Strong programs. Many shelters still rely on manual headcounts and estimated meal totals, which are labor-intensive, error-prone, and insufficient for tracking individual service use over time. This limits operational visibility and makes it difficult to generate reliable reports, identify usage trends, and support external reporting requirements. Shelter Portal addresses this problem by providing a more accurate and privacy-conscious way to document shelter services.

The system was designed as a kiosk and web-based platform that uses scannable QR code cards to …

Grp-02-199 Topological Constraints For Protein Folding, Charles Fanning

C-Day Computing Showcase

We study whether topological loss-based constraints improve multidomain whole-chain protein structure prediction beyond the ColabFold baseline by better preserving the topologies of folded proteins. We benchmark against Wasserstein metrics with our own virtual persistence and RKHS semi-metric constraints as well as higher-order virtual persistence diagrams.

Rethinking Housing Evolution In The Uae: An Investigation Of Sha’Abiat, Socio-Spatial Integration, And Sustainable Urban Development In Al Ain City, Boshra Hassan

Thesis/ Dissertation Defenses

Housing is both a fundamental human need and a core driver of urban form, social life, and cultural continuity. In the United Arab Emirates (UAE), the rapid transformations of the post-oil era were marked by the establishment of the first national housing model, the Sha’abiat, compact, community-oriented neighborhoods that shaped the early urban form of Emirati cities and reinforced social cohesion. Over time, however, housing policies shifted the housing-scape toward private villas, leaving Sha’abiat increasingly challenged by demographic change, maintenance deficiencies, displacement, and demolition under modernization pressures. Despite these challenges, Sha'abiat remains integral to the UAE’s urban identity and provides …

Uc-177-226 Scrapper Kinetics Llc, Mikhail Rudenko, Andrew Torimoto, Jimmy Lock, Marco Cheng

C-Day Computing Showcase

Scrapper Kinetics LLC is a multiplayer and multimodal physics puzzle game. Where players get to choose between playing in VR or Desktop mode, and then, with up to 7 friends (8 players total), try to make a profit in the harsh dead space hulks they have been hired to scrap. We made the game as a test to see how easy it is to have completely different devices interact in the same play space.

Ur-166-204 Multi-User Stem Learning Experience Powered By Llm Conversational Agents, Devon Haynes, Chance Boecker, Oliver Haggard

C-Day Computing Showcase

While Extended Reality (XR) provides experiential and interactive foundations for STEM education, current storytelling and narrative-driven applications often lack responsive nonplayer characters (NPCs), limiting interactive potential through pre-scripted stories. Additionally, despite the growth of Large Language Model (LLM) integration in XR, limited research explores the combined use of multi-user XR systems and conversational Artificial Intelligence (AI) to facilitate real-time, adaptive instruction. This project seeks to address these gaps by 1) Developing a narrative-driven STEM learning XR prototype that incorporates synchronous multi-user interaction and an embedded LLM-driven conversational agent and 2) Exploring the effectiveness of combining these technologies to improve learning …

Ex-04-142 Modeling Distress And Evaluating Chatbot Safety For Suicide-Related Social Media Texts, Rehma Razzak

C-Day Computing Showcase

This project addresses the urgent need for transparent chatbot safety evaluations amid rising concerns about AI-facilitated self-harm. Using public social media datasets, we simulate two tasks: (1) detecting suicidal ideation via emotion-based risk scoring, and (2) stress-testing a support-style chatbot against 888 high-risk prompts, including euphemisms and “for a story” framing. A multi-label classifier trained on GoEmotions feeds emotion profiles into a logistic regression model to generate suicidality risk scores. These scores guide a local chatbot built with Ollama’s llama3, which analyzes user messages and steers responses toward safe, empathetic behavior. Evaluation shows ~90% of replies were safe or supportive. …

Gc-113-125 Carter’S Lake Visitor Center Boating Safety Game, Hunter Blake, Chancelor Brown, Lauren Robbins, Will Bryant, Kendrick Bryant

C-Day Computing Showcase

The Boating Safety Game is an educational, kiosk-based touchscreen game created for the U.S. Army Corps of Engineers and the Carters Lake Visitor Center. It is designed to improve the knowledge and engagement of boating safety concepts for visitors, particularly for students and youth. The project was developed using multiple game scenarios meant to reinforce safe boating practices through tutorial scenes, top down navigation, life jacket and required item selection tasks, and player motivation through quizzes, feedback, scores, and a star ranking system. The game’s design emphasizes accessibility and retention through simple touchscreen interaction, guided instruction, and repeated feedback on …

Gc-141-167 Smart Resume Screening & Interview Preparation Assistant, Loreli Olien, Tomas King, Hanzhi Chen, Kawanda Gray, Brittany Frazier

C-Day Computing Showcase

The hiring process often relies on manual resume review and keyword matching, which can lead to inconsistent and biased candidate evaluations. This project introduces a Smart Resume Screening and Interview Preparation Assistant designed to improve transparency and consistency in early-stage candidate evaluation. The system allows recruiters to upload resumes and job descriptions, then uses embedding-based semantic matching and large language models to assess candidate alignment across skills, experience, education, and projects. The application generates structured rankings, explainable insights, and tailored interview questions. This project focuses on developing a functional prototype that demonstrates how AI can enhance decision support while maintaining …

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.

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.

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 …

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 …

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.

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.

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 …

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 …

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.