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Analysis of Lateral Offset Invariance in Parallel Parking Maneuver: A Geometric Simulation Study

Kevin Luo and Hsinghan Meng,Sunny Hills High School, Fullerton, CA 92833, SAT Professionals, Diamond Bar,CA 91765

ABSTRACT

This paper investigates whether the initial lateral offset of a vehicle from theparking space, denoted ∆y, affects the vehicle’s ability to successfully complete a parallelparking maneuver. Using the geometric mathematical model developed by Wahab et al.[1] and implemented via a simulation developed in a Java-based environment to allow forhigh-fidelity kinematic modelling and real-time geometric validation, a series of trials wereconducted in which ∆y was systematically varied while all other vehicle and parking spaceparameters were held constant. The results demonstrate that, under the constraints of thismodel, the parallel parking maneuver can be completed successfully across a wide range of ∆yvalues. Specifically, varying ∆y causes the geometric solution to self-adjust: the intermediatequantities a, c, y1, and θ all recalculate such that a valid two-arc trajectory always exists.It is concluded that ∆y does not prevent a vehicle from parking; rather, it only shifts thestarting position and arc geometry whi e preserving the feasibility of the maneuver

KEYWORDS

parallel parking, path planning, lateral offset, ∆y, simulation, Ackermann steer ing, bicycle model.

The Illusion of Cybersecurity A Systematic Review of Dynamic Threat Profiles, Attacker Economics, and Adaptive Defense Equilibria

David Mpunwa, ESAMI, Namibia

ABSTRACT

Security teams love the word “hardened.” It is comforting — it suggests permanence, the sense that once a firewall goes up and multi-factor authentication is switched on, the job is finished. That comfort is the problem. This review argues that cybersecurity is not a fixed state but a moving equilibrium, one where attacker economics and defender investment continuously recalibrate against each other. Drawing on the Gordon–Loeb framework and recent evidence on corporate disclosure and reconnaissance costs, the paper traces how commodified cybercrime — ransomware-as-a-service, initial access brokers, dark-web data markets — has collapsed the technical barrier to entry for serious attacks. Core controls (cryptography, multi-factor authentication, VPNs) are mapped against the CIA Triad to show why compliance checklists routinely mistake a snapshot for a state. A layered, Zero-Trust-anchored mitigation roadmap closes the paper.

KEYWORDS

Cybersecurity, Zero Trust Architecture, Threat Actor Economics, Defense-in-Depth, Digital Forensics

The Pyramidal Harmonic Series: Empirical Verification of the Universal Moiré Matrix and Wave Pattern Mathematics

Mark Lance Moody , United States of America

ABSTRACT

Classical wave mechanics rely on continuous algebraic approximations that compress and obscure underlying geometric structures. This paper presents a deterministic framework that natively generates topological wave functions by observing the explicit, uncompressed arithmetic expansions of cyclical partial fractions. By analyzing the reciprocal expansions of specific generating functions—namely the symmetric spatial dilator (10^9 - 1)^2 = 999,999,998,000,000,001 and the asymmetric phase-shift engine (10^8 - 1)(10^9 - 1) = 99,999,998,900,000,001—we demonstrate that explicit arithmetic cascades act as exact mathematical analogs for amplitude stacking and phase precession. When these uncompressed data streams are evaluated within a modulo-72 boundary condition, the output autonomously renders a two-dimensional Moiré interference lattice. Furthermore, by factoring the cyclic limit, we derive a rigid 81-node phase-space combinatorial grid governed by the prime factors 3, 37, and 333,667. This framework proves that positional arithmetic, when fully expanded rather than algebraically compressed, functions as a zero-entropy holographic tensor network.

Use Of Ai-Based Conversational Agents And Postsecondary Student Adjustment And Persistence: Evidence From A Comparative Study Of Ali And Chatgpt

Bruno Kesangana, ¹Department of Teaching and Learning Studies, Faculty of Education Sciences, Université Laval, Quebec City, Quebec, Canada

ABSTRACT

Conversational agents (CAs) are increasingly deployed in postsecondary institutions as accessible, stigma-free supplements to mental health and academic support services. However, the literature rarely distinguishes between institutionally designed, specialized educational CAs and general-purpose large language model (LLM)-based tools such as ChatGPT, treating them as a homogeneous category. This study addresses this gap by comparing Ali — a specialized CA anchored in the Quebec collegiate network — with ChatGPT, a general-purpose LLM, on their relationships with postsecondary student academic adjustment, socio-emotional adjustment, and persistence intentions. Using a quantitative cross-sectional design with 151 students (M_age = 21.86, SD = 1.62; 58.3% female), we conducted ANCOVAs and hierarchical multiple regression analyses. ChatGPT users reported significantly higher competence expectations, perceived value, academic adjustment, socio-emotional adjustment, and persistence intentions. However, regression analyses revealed a more nuanced picture: perceived cost uniquely predicted academic adjustment (β = .196, p = .018); usage duration negatively predicted socio-emotional adjustment (β = −.199, p = .018); and competence expectations predicted persistence intentions (β = .255, p = .012). Moderation analyses showed structurally divergent prediction patterns by CA type: for Ali users, duration, value, and cost positively predicted outcomes, whereas for ChatGPT users, duration negatively predicted socio-emotional adjustment and value showed a negative trend for persistence. These findings challenge the assumption that higher perceived utility translates linearly into better student outcomes and call for tool-specific policies for AI integration in higher education.

KEYWORDS

conversational agents, ChatGPT, Ali, academic adjustment, student persistence, expectancy-value theory, postsecondary education, AI in education

Adaptive Learner Modelling Through Multimodal Behavioural Signals: A Framework For Real-Time Personalization In Higher Education

George Amanortsu ^1,2, Richard Kobla Nyamalor¹,¹ Department of Information Technology, Ghana Communication Technology University (GCTU), Accra,² Ghana Accra Technical University, Accra, Ghana

ABSTRACT

Personalized constrained characterizes learning by static learner profiles that miss moment-to-moment shifts in engagement, comprehension, and motivation. This paper proposes a multimodal learner-modelling framework that fuses clickstream information, response-time patterns, and self-reported affect into continuously updated learner profiles within an AI-driven tutoring environment. Unlike knowledge-tracing approaches that rely on correctness alone, the framework infers latent states such as confusion, disengagement, and cognitive overload to adapt content sequencing, feedback tone, and task difficulty in near real time. The paper detail the architecture, feature set, and a planned semester-long deployment across three undergraduate courses, and specify anticipated outcome ranges grounded in prior meta-analytic evidence. The paper close with design principles for balancing personalization with interpretability and student information privacy.

KEYWORDS

Learner Modelling, AI-Driven Personalization, Adaptive Learning, Multimodal Analytics, Intelligent Tutoring Systems

An Immersive Multiplayer Virtual Reality System to Reduce Family Separation Anxiety During Hospital Stays

Bowen Li1, Andrew Park2 1Jericho Senior High School, 99 Cedar Swamp Rd, Jericho, NY, United States 11753 2University of California, Irvine, Irvine, CA 92697

ABSTRACT

Prolonged hospitalization separates patients from their families at the moment contact matters most, and the telephone and video calls that hospitals rely on place the patient in the role of a spectator rather than a participant. This paper presents FamilyBridge VR, a multiplayer virtual reality application built in Unity in which a hospitalized user and distant relatives inhabit a shared virtual house and perform ordinary domestic activities together. The system joins three subsystems: a hand-tracked interaction layer built on the XR Interaction Toolkit, a session layer built on Photon Fusion, and a set of interactive household props. Two experiments probed the parts of the design most likely to fail quietly. Sweeping the cutoff frequency of the pose-smoothing filter reduced frame-to-frame hand jitter by 93 percent but showed that over-smoothing inflates hold error twenty-eight fold, and measured relay round-trip latency rose from 44 to 199 milliseconds across six geographic tiers, placing intercontinental sessions well beyond the comfort threshold for shared activity.

KEYWORDS

Virtual reality, separation anxiety, pediatric hospitalization, social virtual reality, multiplayer networking, Unity, Photon Fusion, hand tracking, speed-adaptive filtering, family bonding, cybersickness, network latency

An Intelligent Mobile Application to Connect Learners and Mentors Through Skill Exchange Using Flutter, Firebase, and AI Support

Jie Sheng Lee1, Rodrigo Onate2 1Orange County School of the Arts, 1010 N. Main St., Santa Ana, CA 92701 2California State University, Fullerton, 800 N State College Blvd, Fullerton, CA 92831

ABSTRACT

SkillSwap is a Flutter mobile application designed to reduce social isolation by turning peer connection into a practical skill exchange. Users authenticate with Firebase, publish offers, browse nearby or online opportunities, and start real-time conversations when a match looks useful [1]. The home screen also includes a daily mission system that keeps users engaged between exchanges and uses AI-assisted image identification for nature tasks. The project uses Firestore for offers, conversations, and notifications, Geolocator for distance filtering, and OpenAI-powered vision calls for mission validation [2]. The paper examined the app’s challenges, including user safety, location privacy, and notification overload, and compared the design with research on peer mentoring, privacy in mobile social apps, and alert timing. Overall, the system shows how a small app can combine social connection, utility, and lightweight personalization in one workflow.

KEYWORDS

Skill sharing, mentorship, mobile applications, Firebase, geolocation, notifications

An On-Device Mobile System for Translating Consumer EEG Headband Signals into Caregiver-Readable Session States using Bluetooth Low Energy Streaming and Privacy-Preserving Spectral Feature Extraction

Jintian Zhao1, Andrew Park2 1Arroyo Pacific Academy, 325 N Santa Anita Ave, Arcadia, 2University of California, Irvine United States of America

ABSTRACT

Caregivers supporting people who cannot reliably describe their own internal state have few practical tools for noticing rising agitation before it escalates, and clinical electroencephalography is far too costly and immobile for everyday use. This paper presents CerebroSync, a Flutter application that connects directly to a Muse 2 consumer headband over Bluetooth Low Energy, decodes its four-channel electroencephalogram, photoplethysmogram, and inertial packets on the handset, and reduces each one-second window to relative band power, electrode contact quality, a pulse estimate, and a single readable session label. Raw neural signal never leaves the device; only one compact feature checkpoint per minute reaches owner-scoped cloud storage. Two experiments driving the shipped signal processor show that the label boundary sits near an alpha-to-beta power ratio of 1.06 to 1.10 rather than at parity, that labels remain unstable across a wide ratio band, and that the pulse estimator fails specifically at bradycardic rates. Honest reporting of these limits is the contribution.

KEYWORDS

Electroencephalography, consumer neurotechnology, Muse 2, Bluetooth Low Energy, on-device signal processing, spectral band power, photoplethysmography, mobile health, Flutter, neural data privacy, edge computing, caregiver supports