Douglas G. Stevenson, Civilian Scientist, United States of America
tevenson-Flux Information Theory (SFIT) proposes that gravity is a dynamic, informationcarrying flux oscillating at the geometric resonance frequency νres = 1.20134 mHz with coupling kernel K = 1.060. This non-reciprocal metric perturbation couples classical gravity to quantum systems and produces observable laboratory signatures, including a 14.28σ modulation in ultra-cold neutron data, Kohlrausch–Williams–Watts (KWW) relaxation tails with β = K, and quantized information flux analogous to M2-brane charge quantization in M-theory. We present the complete mathematical framework, derive the flux quantization condition from single-valuedness of the neutron wave function, establish the information-theoretic origin of the KWW stretched exponential, and show how SFIT provides a low-energy effective realization of holographic and ER=EPR principles. The theory is fully laboratory-testable and offers a new information-theoretic bridge between general relativity and quantum mechanics. All data, code, and synthetic event generators are openly available.
Information Theory, Resonant Flux, Non-Reciprocal Metric, KWW Relax-ation, Flux Quantization, Holographic Duality, Quantum Gravity, Ultra-Cold Neutrons, Machine Learning Applications
Jered McClain , SEXA Institute of Technology and Interdomain Galactic Advisors,United States of America
This paper formulates Recursive Information Admissibility (RIA), a traceable constraint architecture for multivariate information validation, fault localization, and loss-aware dimensional reduction. Prior mechanical evidence is extended with a controlled four-task information benchmark and an independent public-data novelty study. In the controlled suite, RIA reduced macro false admission to 35.08%, outperforming LOF at 42.75%. Across 17 public novelty tasks and 306 evaluations, however, generic RIA did not lead: false admission was 78.61% versus 73.61% for Isolation Forest, while LOF achieved higher ROC-AUC. The combined result narrows the supported claim: RIA is promising when domain relationships and invariants are explicitly known, but is not demonstrated as a universally superior anomaly detector.
Recursive Information Admissibility, Information Validation, Artificial Intelligence, Knowledge, Representation, Information Integrity
Arman Shahpari and Arezou Sadoughi, Appalachian State University, USA
Artificial intelligence (AI) is increasingly discussed as a participant in the construction workforce rather than merely a software tool. This paper conducts a systematic critical review of AI applications across the construction lifecycle — pre-construction, construction, and operations/post-construction — and proposes an “Augmented Workforce” taxonomy that classifies AI systems by their degree of autonomy relative to human tasks (Human-only → AI-assisted → AI-augmented → Human-supervised AI autonomy → High-autonomy task execution). Using a PRISMA-style review protocol, we screened records from Scopus, Web of Science, Engineering Village, ScienceDirect, and Google Scholar (search window and terms reported in Section 2), and we introduce a five-level evidence hierarchy (A–E) to separate independently validated academic findings from vendor-reported claims. We further propose a quantitative validation framework linking AI performance metrics (precision, recall, F1-score, mAP) to construction-management KPIs (schedule variance, cost variance, rework rate, detection lead time). Findings indicate that current evidence robustly supports task-level augmentation but does not yet establish broad autonomous workforce substitution. We identify gaps in reproducible benchmarking, economic analysis, and governance research, and propose a structured research agenda.
artificial intelligence, construction management, augmented workforce, systematic review, evidence hierarchy, quantitative validation framework
David Allan Nelms, Independent Researcher, Inverness, Mississippi, USA
Extended language-model workflows can lose task constraints, reuse superseded decisions, promote hy-potheses into facts, or misreport completed work. This paper specifies the ATLAS Autonomous Drift Con-tainment System, an architecture that treats these failures as unauthorized state transitions. Its components are a versioned project baseline, typed evidence records, authority-aware updates, independent checks, controlled repair, and recoverable checkpoints. The contribution is a reproducible transition protocol and evaluation design, rather than a new memory mechanism or a claim of universal reliability. A conditional invariant-preservation argument defines the controller’s enforcement boundary. A small deterministic im-plementation exercises ten scripted cases and additional recovery checks; it demonstrates conformance of those rules without evaluating an LLM. A controlled study is proposed to measure drift, legitimate-update acceptance, task completion, and cost against matched baselines. Effectiveness in language-model work-flows remains unvalidated.
Large language models, Operational drift, State management, Provenance, AI reliability
Yushi Liu 1, Armando Contreras 2 , 1 Hangzhou International School, 2190 Xiangbin Road, Binjiang District, Hangzhou 310052 , 2 University of California, Irvine, Irvine, CA 92697
Learning an instrument can be difficult because students often practice alone, cannot identify mistakes, and may lose motivation. I created BlueNote, a Flutter mobile app designed to help jazz musicians practice more effectively and independently [1]. Users choose their instrument, follow practice plans, receive Pitch Coach feedback, and track Time, Tone, Technique, and Tuning. The app uses Dart, phone microphone access, audio signal processing, pitch detection, instrument transposition, navigation, and local progress storage. Major challenges included inaccurate note detection, background noise, and differences between B-flat, E-flat, and concert-pitch instruments. I improved filtering, stabilized readings, and added instrument-specific settings [2]. During testing, I compared pitch accuracy in quiet, classroom, and busy environments, and compared app scores with teacher scores. Accuracy averaged 98%, 88%, and 69.8% across those environments. App scores differed from teachers by 3.2 points on average. BlueNote is useful because it gives musicians clear, immediate, organized feedback whenever a teacher is unavailable.
Pitch detection, Music education, Real-time feedback, Instrument transposition, Mobile application
Shengbo Zhou 1, Austin Amakye Ansah 2 , 1 Santa Margarita Catholic High School, 22062 Antonio Pkwy, Rancho Santa Margarita, CA , 2 The University of Texas at Arlington, 701 S Nedderman Dr, Arlington, TX 76019
College applicants often need both reliable information and personal guidance, yet comprehensive private counseling can require substantial financial commitment. CollegePeer is a browser-based platform that connects high school students with university students for individually priced, one-to-one mentoring sessions. The platform combines mentor discovery, school-email verification, payment processing, and real-time communication within a single workflow. Its architecture separates a Next.js application and server API from Supabase authentication, PostgreSQL data storage, Stripe payment services, email delivery, and WebRTC media transport [11]. Mentor recommendations use stated university and major preferences, while verification links establish access to an accepted institutional mailbox. Server-side checks and database policies restrict sensitive operations; Stripe Checkout and Connect support student payments and mentor payout routing. WebRTC handles audio and video, with Supabase signaling and short- lived TURN credentials supporting connection establishment across different networks. This paper describes implementation, explains the principal trust and coordination challenges, and compares the design with empirical research on college access, student coaching, and virtual advising. The contribution is an integrated engineering approach to providing access to near-peer guidance. Educational effectiveness, long-term affordability, and advising quality remain questions for future user study rather than established outcomes of the implementation.
College access, near-peer mentoring, WebRTC, identity verification, online advising, Supabase
Jiaqi Feng 1, Kyler Harris 2 , 1 Lawrence academy, 26 powderhouse road, Groton, MA 01450 , 2 University of California, Santa Cruz, 1156 High St, Santa Cruz, CA 95064
Students facing anxiety, depression, or everyday emotional distress often lack a single, low-friction starting point for support, caught between under-resourced school counselors, intimidating crisis hotlines, and costly professional therapy. This paper presents Haven Circle, a mobile application built with Flutter, Firebase Authentication, Cloud Firestore, and Firebase Cloud Functions that unifies mood tracking, journaling, real-time peer volunteer matching, a self-help resource library, a multi-source counselor directory, and a safety-scoped AI conversational assistant in one system [6]. Key engineering challenges included preventing duplicate volunteer matches through Firestore transactions, grounding free-text location input in real coordinates before querying Google Places, and constraining an AI assistant to reliably redirect crisis language toward emergency resources. Two experiments evaluated the AI Helper’s crisis-response reliability (92.5% accuracy across 40 test messages) and the effect of explicit location bias on search proximity (average result distance fell from about 42 to 6 miles) [7]. Compared to existing single-purpose peer-support, AI-chatbot, and crisis-monitoring apps, Haven Circle’s integration of all three into one real-time backend offers students a more complete and realistic path toward help.
Mobile mental health, Peer support, Flutter, Firebase Firestore, Real-time matching, Conversational AI, Geolocation search, Adolescent well-being
Trần Thụy Minh Thư, Western Sydney University , Vietnam
This study applies the Stimulus-Organism-Response (S-O-R) framework to analyze the relationship between the Communication Quality of AI Chatbots (CQA) and the Continuance Usage Intention (CUI) among young consumers in Vietnam’s fashion sector. The proposed research model suggests that CQ influences CUI through three consumer responses: Brand Loyalty (BL), Brand Preference (BP), and Customer Satisfaction (SA). Survey data from 477 respondents were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results indicate that all dimensions of CQ have a positive relationship with BL, BP, and SA. Mediation analysis further reveals that BL, BP, and SA serve as critical mechanisms linking CQ to CUI (with the exception of the indirect effect mediated through communication competence). This study contributes to a more nuanced understanding of CUI as a multidimensional outcome; it stems not only from communication quality but also from the process of transforming interactive experiences into brand-related responses and evaluations. Consequently, the study broadens the perspective on AI chatbots, shifting the conceptualization from a mere technological tool to a brand touchpoint thereby bridging the gap between technological experience.
Communication Quality, AI Chatbots, Continuance Usage Intention, Brand Loyalty, Brand Preference, Customer Satisfaction.
Ismael Fortunado,University of Santo Tomas, Philippines
3n +1 is integer, n/2 is integer and other numbers such as 3,7, etc. are integer. The problem is in in which (iteration) one number belongs. After the upscale, there is satisfying the inequality because numbers are rooted to 1. Distinguishing addition after multiplication from division, (From an upscale), numbers missed certain margins for them to have a cycle or be unbounded. 3n+1 is addition and n/2 is division, How do we arrange (n – 1)/3 = x/2y, the equation? And two successive n/2 is n/4 and all other arrangements. Each math problem usually or could have an inequality such as the conjecture. The problems of the Collatz conjecture were solved. Introduction of the inequality and complementing of the algorithms from and form bases were used. The center of the problem/s was basically solved by the analysis or presence of the inequality. “The lowest number in a suspected or assumed cycle has a lower number which is a contradiction to the counterargument or a number proof that the conjecture is false. Thus, not making another cycle-(s).” and thus reaching 1. The inequality is Odd step < Following Two Successive Even Steps or approximately equivalent, (only equals at the trivial cycle). Thus, not making the number to reach infinity. For a cycle to be relevant, “The head must be even and the tail must be odd.” If the head is odd, it has a higher number because of the application 3n+1; if the tail is even, it has a lower number for the application of n/2. The inequality matches the analysis. Higher numbers have additional algorithm/s from lower base numbers which is a complement to the inequality. Based on the inequality, the numbers failed to see another cycle but a lower number. It is like a skipping step where the end result is a 1 (easily visible with n+1).
Collatz coin, Traps, Cycles, Infinites, Flows
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