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Item type:Item, New Brunswick Population and Demographic Counts Update: 2025 Data(DataNB, 2026-08-28) Beykzadeh, Ali; Jones, Bethany; McDonald, Ted; Miah, PabloThis seventh report in DataNB’s population and demographic counts series provides an annual update on New Brunswick’s population from January 2022 through December 2025. Drawing on administrative data, the report presents total population counts, demographic and geographic characteristics, and migration flows into and out of the province. The analysis highlights both growth and decline across different categories of residents, including Canadian citizens, permanent residents, and temporary residents such as study and work permit holders. Findings show that New Brunswick’s population reached 861,040 at the end of 2025, continuing a trend of annual growth but at a slower pace than in recent years. First time arrivals declined sharply in 2025, particularly among non citizens, coinciding with increased federal limits on immigration that are expected to expand in coming years. Out migration also increased among study and work permit holders.Item type:Item, Sensitivity-based analyses of WECC load model parameter identification accuracy under various voltage dip characteristics(University of New Brunswick, 2026-06) Joy, Sakib Shahriar; Cardenas Barrera, Julian L.; Rahimi, TohidThis thesis presents a sensitivity-based parameter identification framework for the WECC Composite Load Model under varying voltage sag conditions. The developed MATLAB implementation of the WECC model, including simplified DER_A, is first validated through frame-by-frame comparison with the standard WECC reference model, showing excellent agreement in both active and reactive power responses. The original model consists of 56 parameters for combined active and reactive power estimation. Through the application of a windowed normalized RMS-based sensitivity analysis, this parameter set is reduced to 38 parameters, representing an approximate reduction of 32.1% and significantly reducing the search space. Particle Swarm Optimization is then applied for parameter estimation under multiple disturbance scenarios. The optimized results closely match the reference responses, demonstrating that reduced-parameter estimation maintains high accuracy while improving computational efficiency. The proposed approach enables more robust and practical parameter identification for large-scale power system studies.Item type:Item, AI-driven multimodal triage system for early ICU risk prediction in emergency departments(University of New Brunswick, 2026-06) Malachi, Olaoluwa; Light, JanetArtificial Intelligence has been redefining the healthcare industry through the use of complex algorithms and large-scale clinical data to identify predictive patterns that can reduce human error and optimize clinical decision-making compared to traditional triage methods. Traditional triage systems such as CTAS, ESI, and MTS primarily rely on subjective clinical judgment and are designed to assess immediate acuity scale rather than predict short-term clinical deterioration. In this thesis, a calibrated multimodal AI-driven triage decision-support framework is proposed for predicting ICU admission within 24 hours of emergency department presentation. To predict high risk patients, structured triage variables, free-text clinical complaints, and short-horizon temporal physiological trends are extracted from large-scale electronic health record datasets, including MIMIC-IV and eICU. Risk estimation is performed using calibrated gradient-boosted tree models, text-based classifiers, and temporal sequence-based neural modeling to produce interpretable and operationally reliable predictions. In emergency settings, calibration techniques ensure that predicted probabilities correspond closely to true outcome likelihoods, enabling safer prioritization under uncertainty. Model performance is evaluated using both discrimination and calibration metrics, reflecting predictive accuracy and probabilistic reliability under imbalance. To assess operational relevance, a novel capacity-aware simulation framework is developed to compare traditional triage ordering with AI-based prioritization under constrained resources. Results indicate that the proposed system improves early identification of high-risk patients and supports more effective prioritization decisions in emergency care.Item type:Item, An adaptive hybrid intrusion detection framework for industrial Iot: Architecture, datasets, and learning techniques(University of New Brunswick, 2026-06) Firouzi, Amir; Ghorbani, Ali A.The rapid proliferation of the Internet of Things (IoT) in industrial domains has led to the Industrial Internet of Things (IIoT), enabling unprecedented levels of automation, connectivity, and efficiency. However, the integration of heterogeneous sensors, embedded devices, and network infrastructures significantly expands the attack surface of cyber-physical systems and exposes them to diverse security threats. Securing these environments requires anomaly detection that is accurate, scalable, lightweight, and adaptive under real-time and resource-constrained conditions. This thesis proposes DeepSense, a hybrid multi-layer intrusion detection framework designed to provide scalable and adaptive IIoT anomaly detection. It integrates three components within a layered architecture. RuleSense performs lightweight rule-based detection at the network edge to rapidly filter suspicious activity with minimal overhead. NeuroSense applies machine learning and deep learning models for fine-grained attack classification and improved accuracy. DataSense provides an IIoT testbed and dataset with realistic benign traffic and 50 attack types across seven categories to support training and evaluation. Together, these components balance low-latency edge filtering with accurate large-scale analysis. To improve adaptability and operational efficiency, the framework further incorporates several supporting mechanisms. An adaptive scalable ensemble continuously monitors detection performance, identifies concept drift, and dynamically triggers model retraining or rule reprofiling as needed to maintain robustness under evolving attacks and constrained device coverage. A multi-objective feature selection method reduces dimensionality while preserving detection quality, improving computational efficiency and scalability. The thesis also introduces a performance evaluation framework that assesses IIoT anomaly detection across Detection Quality, Speed and Latency, Coverage, and Resource Usage. Using normalized metrics and multi-criteria decision-making, it supports fair comparison and ensemble selection. Experiments show that RuleSense exceeds 99% detection accuracy with minimal overhead, and NeuroSense achieves strong performance in both 8-class and 50-class settings. The adaptive ensemble further improves resilience under constrained deployment. Overall, DeepSense provides a principled and practical framework for IIoT anomaly detection by integrating lightweight edge-level filtering, intelligent learning-based classification, adaptive ensemble optimization, and systematic evaluation to address key limitations of existing IIoT security solutions and establishes a robust foundation for securing industrial systems against both known and emerging cyber threats.Item type:Item, Post-printing heat treatment routes for additively manufactured 420 stainless steel(University of New Brunswick, 2026-06) Bongao, Harveen C.; Aranas, ClodualdoThe introduction of new steel alloys into additive manufacturing requires the development of appropriate post-processing strategies to tailor microstructure and mechanical performance. Additively manufactured 420 stainless steel (AM420SS) has recently emerged as a martensitic steel alloy suitable for processing by laser powder bed fusion (LPBF). Despite exhibiting mechanical properties comparable to those of conventionally heat-treated counterparts in the as-printed condition, post-printing treatments remain necessary to maximize in-service performance and broaden industrial applicability, either by exploiting the solidification-induced microstructure or by homogenizing the microstructure to eliminate solidification features while enabling controlled phase constitution. In this thesis, four post-printing strategies are established. The first strategy is a quench-and-partitioning (QP) treatment designed to homogenize the microstructure and modify the austenite grain morphology. The results reveal that a short austenitization duration followed by initial quenching to 150°C during QP promotes the formation of Σ3 twin boundaries in the high-temperature austenite state and leads to the development of inter-lath austenite within individual parent grains under ambient conditions. The second strategy involves a systematic investigation of direct tempering (DT) for austenite reversion. Microstructural characterization shows that increasing the DT temperature from 300°C to 500°C drives a transition in reverted austenite from Type I (located between neighboring martensitic laths) to Type II (located along parent austenite grain boundaries). The third strategy is a quench-and-tempering (QT) treatment that transforms the microstructure into fine and fully-martensitic grain state. Mechanical property evaluation of QT and DT samples tempered at 400°C for 30 minutes demonstrates that transformation-induced plasticity is activated only in DT-treated samples containing both Type I and Type II reverted austenite microstructure. In addition to these thermal treatments, the fourth strategy is through thermo-mechanical processing which examines softening mechanisms active during the high-temperature deformation. Dynamic recrystallization is the dominant mechanism at intermediate temperature and strain rate conditions, while dynamic recovery through grain growth takes over at high temperatures. Overall, the findings of this thesis demonstrate that the microstructure and mechanical performance of LPBF-fabricated AM420SS can be engineered through different post-printing routes to meet the requirements of high-strength applications.
