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

Kevin Luo and Hsinghan Meng, 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

Insight - Driven business rules for operational knowledge

Rajeev Kaula

ABSTRACT

Business process intelligence improves operational efficiency that is essential for achieving business objectives, besides facilitating competitive advantage. As organizations operate through inter-connected business processes, insights into their process performance through related business rules are essential to achieve business objectives. This paper outlines an approach for developing insight-driven business rules using the concept of buckets, which can lead to the development of a repository of business knowledge for business process operations. The proposed concepts are demonstrated through a prototype modeled on a hypothetical customer mortgage lending process, implemented using Oracle’s PL/SQL database language.

KEYWORDS

Business Intelligence, Process Intelligence, Business Process, Business Rules, Oracle, PL/SQL

A Personalized Family-Activity Recommendation System for Reducing Planning Burden in Parents with High Perfectionistic Concerns and Intolerance of Uncertainty

Oscar Li and Yu Sun, Bellevue, Washington 98008 California State Polytechnic University, Pomona, CA, 91768

ABSTRACT

Parents characterized by high perfectionistic concerns and low tolerance for uncertainty often absorb the entire burden of planning and executing family activities. Perfectionism is an established risk factor for parental burnout, and the cognitive labor of planning — anticipating needs, identifying options, deciding, and monitoring — is unevenly distributed within households. We present a recommendation system that suggests family activities matched to a households constraints and traits, paired with a delegation protocol in which the affected parent deliberately does not manage execution. Because outcome data was not collected at pilot scale, we evaluate the system on simulated data whose generating rules are stated explicitly, and we frame the evaluation as a test of learnability and data requirements rather than as evidence of therapeutic effect. Across 1,000 simulated household–activity pairs we compare seven regression models against a mean-prediction baseline, ablate feature blocks, and characterize degradation under increasing self-report noise. Activity-side features carry nearly all predictive signal; flexible models lose their advantage over linear regression once outcome noise exceeds approximately 15 points; and trait-based personalization provides no ranking advantage over a global popularity baseline under a severity-only model of the trait. We show this last result follows from a structural property of the interaction rather than from the choice of model, and identify the condition a trait must satisfy for personalization to be worthwhile.

KEYWORDS

Recommender systems, Machine learning, Scikit-learn, Parental burnout, Perfectionism, Cognitive labor, Simulation, Intolerance of Uncertainty

An Instrument-Aware Mobile Rehearsal Platform to Synchronize Ensemble Score Reading Across a Classroom

Winnie Gu¹ Rodrigo Onate^{2 1}Northwood High School, 4515 Portola Parkway, Irvine, CA 92620 ²California State University, Fullerton, 800 N State College Blvd, Fullerton, CA 92831

ABSTRACT

Ensemble music classrooms lose rehearsal minutes every time players lose their place in a long multi-part score, and the loss falls hardest on the least experienced readers. This paper presents Maestro, a mobile rehearsal platform that ingests a conductor’s score PDF, separates it into one part per player, and drives every student’s device from a single tempo clock so the whole room reads the same measure at the same moment. A Python server runs a dedicated optical music recognition engine and a symbolic music toolkit to produce notation, per-part page geometry, and MusicXML; a cross-platform client renders each student’s own part as a cropped page, a synthetic staff, or engraved notation over one shared beat clock; a cloud document broadcasts the instructor’s tempo and start timestamp. The hardest problem is that no device clock is guaranteed to agree with any other, which a server-issued timestamp would resolve. Two experiments against the shipped code show that a 250-millisecond clock offset places 14.2% of instants on the wrong note, and that the assumed four-beat measure holds for only 72.7% of 1,644 recognized measures.

KEYWORDS

Optical music recognition, Ensemble rehearsal, Music education technology, Score following, Live synchronization