School of Engineering, Computer and Mathematical Sciences - Te Kura Mātai Pūhanga, Rorohiko, Pāngarau

Permanent link for this collectionhttps://hdl.handle.net/10292/553

AUT is home to a number of renowned research institutes in engineering, and computer and mathematical sciences. The School of Engineering, Computer and Mathematical Sciences strong industry partnerships and the unique combination of engineering, computer and mathematical sciences within one school stimulates interdisciplinary research beyond traditional boundaries. Current research interests include:

Artificial Intelligence; Astronomy and Space Research;

Biomedical Technologies;

Computer Engineering; Computer Vision; Construction Management;

Data Science;

Health Informatics and eHealth;

Industrial Optimisation, Modelling & Control;

Information Security;

Mathematical Sciences Research; Materials & Manufacturing Technologies;

Networking, Instrumentation and Telecommunications;

Parallel and Distributed Systems; Power and Energy Engineering;

Software Engineering; Signal Processing; STEM Education;

Wireless Engineering;

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Now showing 1 - 20 of 1901

Experimental Results From the Cryogenic Cooling of a Rotor Using an Internal Pump
(IOP Publishing, 2026-04-01) Caughley, AJ; Lumsden, G; Badcock, R; Gschwendtner, M; Jeong, S
Abstract: Superconducting motors are a route to the high power-to-weight ratio required for the electrification of large aircraft. In a synchronous superconducting motor, a popular configuration is to have the rotor with DC field coils and the stator with AC coils. This configuration makes the rotor cooling easier, as DC superconducting coils have few losses. However, the heat from the rotor still needs to be transferred across a high-speed rotating interface. Our proposed cooling method uses a helium gas circuit, internal to the rotor, that is circulated by the rotor’s motion against that of a cold stationary heat exchanger. Keeping the cold heat exchanger stationary reduces sealing requirements as the internally pumped gas can be kept near ambient pressure whilst the cold heat exchanger could be cooled by either pressurised cryogenic fluid, a two-phase medium, or a cryocooler. The internal rotor pump concept was first validated with a CFD model, which was in turn confirmed by experimentation. This paper presents the results of the proof-of-concept experiments that validated the CFD model and will present further improvements to the concept, demonstrating a feasible cooling method and its application to a superconducting rotor.
Smart Grid Electricity Demand Forecasting Using Weather-based MIDAS and Machine Learning Models: The Case of New Zealand
(Springer Science and Business Media LLC, 2026-06-02) Ramesh Babu, Rogith; Wichitaksorn, Nuttanan; Su, Shu; Cortes Pires, Clarissa; Lu, Edna
This study develops a comparative forecasting framework that integrates daily weather information with quarterly electricity generation, used here as a proxy for electricity demand in New Zealand, through mixed-frequency modelling approaches. The analysis progresses from baseline univariate time-series models to classical mixed data sampling regressions, advanced regularised and autoregressive mixed-frequency models, and machine learning-based mixed-frequency methods. The forecasting results show that mixed-frequency models can improve upon traditional univariate benchmarks by incorporating higher-frequency weather information. Among the advanced approaches, autoregressive mixed-frequency models deliver strong forecasting performance, particularly over shorter recent evaluation windows, while seasonal time-series benchmarks such as SARIMA remain highly competitive and achieve the lowest RMSE in the main eight-quarter evaluation period. Machine learning-based mixed-frequency models show mixed performance, likely reflecting the challenges posed by data dimensionality and limited sample size. The proposed framework provides interpretable forecasts and offers practical insights for electricity system planning in renewable-dominated energy systems.
Multi-Channel Enhanced Synchro-Reassignment Transform for Robust Identification of Sub/Super-Synchronous Oscillations Using Synchronized Measurements
(Institute of Electrical and Electronics Engineers (IEEE), 2026-06-08) Wang, Lixin; Niu, Tianhong; Sun, Zhenglong; Gao, Han; Jiang, Shouqi; Cai, Guowei; Lie, Tek Tjing
Synchrophasor-based sub-synchronous oscillations (SSOs) parameter identification is effective for monitoring SSOs, while its performance is significantly degraded by measurement noise, posing challenges to reliable parameter identification. This paper proposes a novel technique using multi-channel enhanced synchro-reassigning transform (MESRT) for robust and accurate SSO parameter identification. First, each measurement channel is transformed using the short-time Fourier transform (STFT) to construct a global multi-channel time-frequency (TF) representation. Then, an average modal energy-based strategy is developed to eliminate noise-induced spurious modes in the STFT spectrum. Subsequently, a three-step selection rule is applied to extract TF coefficients representing oscillation modes, from which the time-domain components are reconstructed. Finally, the Hilbert transform (HT) method is employed to identify the oscillation frequency and damping factor of each mode. The proposed MESRT method effectively improves the noise immunity and accuracy of conventional SRT by considering inter-channel correlations and leveraging modal energy to remove spurious modes. Case studies validate that the proposed method performs exceptionally well in terms of accuracy and noise robustness, demonstrating superior performance compared to existing methods.
Automated Risk Assessment of Opioid Use: Analysis Using Pre-trained Transformers on Social Media Data
(JMIR Publications Inc., 2025-05-20) Ahmad, Muhammad; Orji, Rita; Amjad, Maaz; Siddique, Abubakar; Kubysheva, Nailya; Batyrshin, Ildar; Sidorov, Grigori
BACKGROUND: The illegal use of opioids has emerged as a major global public health concern, contributing to widespread addiction and a growing number of overdose-related deaths. In response, the US federal government has invested billions of dollars in combating the opioid epidemic through treatment, prevention, and law enforcement initiatives. Despite these efforts, there remains an urgent need for automated tools capable of detecting overdose cases and assessing the risk levels of substances-tools that can enable faster, more effective responses with less reliance on human intervention. Social media, particularly Reddit, has become a valuable source of self-reported data on opioid misuse, offering rich insights into user experiences and symptoms. OBJECTIVE: This research aimed to develop an advanced automated tool for detecting opioid overdose risks and classifying substances into high-risk and low-risk categories by analyzing social media posts. METHODS: A multistage methodology was used to achieve the objectives of this work. First, a new dataset was constructed from Reddit posts and manually annotated. Each post was labeled according to the risk level of the mentioned substance, using contextual indicators and user-reported experiences as the basis for classification. To ensure reliability and annotator consistency, detailed annotation guidelines were developed and applied throughout the labeling process. Second, a bidirectional encoder representation from transformers for biomedical text mining (BioBERT)-based classification framework was implemented and enhanced with a custom attention mechanism to capture relevant semantic information for more accurate predictions. Third, the model's performance was evaluated using 5-fold cross-validation and compared against several baseline approaches, including traditional supervised learning, deep learning, and transfer learning methods. In total, 14 experiments were conducted to evaluate comparative effectiveness. To further assess the contribution of the attention layer, the best-performing model was also evaluated against a version incorporating the standard self-attention mechanism, using a train-test split. Finally, a paired t test was conducted to statistically assess the performance difference between the BioBERT-based model and the strongest baseline, extreme gradient boosting (XGBoost), providing validation of the observed improvements. RESULTS: The proposed BioBERT model with custom attention achieved an F1-score of 0.99 in cross-validation, outperforming the best baseline, XGBoost (F1-score=0.97), with a relative improvement of 2.06%. A paired t test conducted across the 5 folds (n=5) confirmed that the performance gain was statistically significant (P=.003), providing strong evidence that the improvement reflects genuine advances in overdose risk detection. CONCLUSIONS: This paper demonstrates the potential of leveraging social media data and advanced natural language processing models to build reliable systems for opioid overdose risk detection. The BioBERT model with custom attention shows state-of-the-art performance and robustness, offering a powerful tool to support timely intervention and harm reduction strategies in the ongoing opioid crisis.
Real-time Cavity Volumetry via Helmholtz Resonance Using Pressure Amplitude: Proof of Concept
(Springer, 2026-05-29) Barzegar, Mohammad Amin; Davies, Clive; Grafton, Miles
Helmholtz resonance provides a well-established acoustic basis for determining volume via the resonance-frequency–volume relationship. However, frequency-tracking methods are typically too slow for dynamic measurements. We present an alternative physical model, the sound-pressure quality-factor (SPQF) model, which estimates volume in real time from cavity sound-pressure amplitude, avoiding frequency hunting. The model follows from the equations governing the driven, underdamped vibration of the port-air mass. The resonator is excited at its empty-cavity natural frequency with a single-tone drive; inserting a sample reduces the steady-state pressure amplitude, from which displaced volume is inferred. We validate the method with liquid and solid samples in 1-, 2-, and 3-L cavities and in a mechanically adjustable chamber under dynamic conditions. The approach achieved millilitre-level accuracy for solids and relative expanded uncertainty U, k = 2 < 0.1% of cavity capacity in static tests, and it tracked liquid discharge at ~ 15–20 Hz. On the mechanically variable resonator, SPQF tracked piston-driven volume changes for speeds up to 75 mm·s⁻1, delivering ~ 20 measurements in 1.5 s.
Fairness-Constrained Influence Maximisation via Multi-Objective Optimisation
(Elsevier BV, 2026-06-02) Zhao, Ziying; Li, Weihua; Ma, Jing; Jiang, Jianhua; Bai, Quan; Gu, Wen
The Influence Maximisation (IM) problem seeks to select a set of seed nodes to maximise information diffusion in a network. While existing approaches have achieved significant improvements in overall diffusion, they often overlook fairness across communities, which can result in biased dissemination and the exclusion of disadvantaged groups. To address this, we define the Fair Multi-objective Influence Maximisation (FMOIM) problem, which jointly optimises influence spread and equity fairness. Equity Fairness is modelled at the community level as the alignment between the realised diffusion-benefit distribution and a desired reference allocation. Jensen–Shannon divergence (JSD) similarity quantifies distributional deviation from the reference allocation, while Jain’s fairness index characterises the evenness of benefit allocation across communities. To solve FMOIM, FairWolf is proposed as a problem-driven discrete multi-objective optimisation model for fairness-aware influence maximisation. It reformulates the Grey Wolf Optimiser dynamics to search directly over fixed-budget seed sets under community-level fairness objectives, capturing the spread and fairness trade-off. FairWolf incorporates three components: (i) a discrete position-updating mechanism tailored to seed-set construction, (ii) an Explorer-Augmented Leader Selection strategy that enhances population diversity while maintaining convergence pressure, and (iii) a Hypervolume (HV)-triggered perturbation mechanism that adaptively mitigates stagnation in non-convex multi-objective search spaces. Experiments on eight real-world networks demonstrate that the FairWolf model consistently outperforms state-of-the-art baselines, yielding a higher HV value and more uniformly distributed Pareto fronts. These results demonstrate its effectiveness and practicality for fairness-aware diffusion in applications such as viral marketing, public health, and resource allocation.
A CTI-Enriched GCN-LSTM Architecture for Multiclass Cyberattack Classification in Critical Infrastructure
(MDPI AG, 2026-06-03) Pinto, Andrea; Herrera, Luis-Carlos; Donoso, Yezid; Gutierrez, Jairo
Critical infrastructures (CI) are essential to modern society, providing vital services such as energy, water, and transportation. However, these systems are increasingly targeted by sophisticated cyberattacks, exploiting vulnerabilities in both IT (Information Technology) and OT (Operational Technology) environments, posing significant risks to safety, economic stability, and national security. Despite advancements, current anomaly detection models for CI often cannot effectively integrate diverse data sources or provide detailed attack classifications. To address these challenges, we propose a novel Graph Convolutional Network (GCN) model integrated with Long Short-Term Memory (LSTM) layers for effective anomaly detection and attack classification in CI. The model leverages Cyber Threat Intelligence (CTI) and MITRE ATT&CK techniques, integrating network traffic and physical device data to enhance detection of sophisticated threats. Unlike approaches using binary classification, our model performs multiclass classification to recognize specific attack types, bridging the gap in understanding complex attack patterns within CI. By incorporating Indicators of Compromise (IoCs) from MISP (Malware Information Sharing Platform) with the SWAT (Secure Water Treatment) dataset, we developed a graph-based data structure where nodes represent entities like SCADA tags and IP addresses. The model processes this dynamic graph using convolutional layers for spatial feature extraction and LSTM layers for temporal dependencies. Results indicate a significant improvement over existing solutions, achieving a test accuracy of 99.04% and a macro F1-score of 0.9151. The integration of multiple data sources enhances the model’s capacity to handle evolving cyber threats, making it well-suited for protecting CI.
A Lightweight IoT Healthcare Wearable for Fall Detection and Ambient Hazard Sensing
(Elsevier BV, 2026-06-02) Sabit, Hakilo
Rapid advancements in digital and embedded technologies have transformed modern healthcare, enabling innovative approaches to continuous patient monitoring. Caring for elderly individuals presents ongoing challenges, particularly when caregivers cannot remain physically present to provide support. This project addresses this need by developing an Internet of Things (IoT)-enabled wearable monitoring system capable of delivering real-time access to key health and environmental indicators. The proposed device integrates multiple sensors to monitor vital signs and safety-related events, including fall detection, and thermal comfort parameters. Detected events—such as abnormal temperature levels or sudden falls—trigger immediate alerts, ensuring timely intervention during emergencies. All sensor readings are transmitted to a web server, where data are processed and presented through an accessible dashboard for remote monitoring. This work demonstrates a proof-of-concept wearable platform designed to enhance caregiver awareness, improve responsiveness, and support safer independent living for elderly individuals. The system provides a foundation for future development in IoT-based healthcare monitoring solutions, offering the potential for scalable and continuous oversight of vulnerable populations.
DPCI-GPSR: A Directional Propagation Capacity Index for Enhanced GPSR Routing in VANETs
(MDPI AG, 2026-05-18) Liu, Yue; Al-Hamid, Duaa Zuhair; Li, Xue Jun
Vehicular ad hoc networks (VANETs) enable direct wireless communication between moving vehicles for safety and cooperative driving. Routing in VANETs is challenging due to high mobility, frequent topology changes, and variable node density. The Greedy Perimeter Stateless Routing (GPSR) protocol maintains only a one-hop neighbor position table through periodic beacon exchanges, making it highly scalable. Each node forwards packets to the neighbor geographically closest to the destination. However, this distance-only criterion leads to a low packet delivery ratio (PDR). Existing improvements, such as Weight-Based Path-Aware GPSR (W-PAGPSR) combining distance progress, velocity direction, neighbor density, and link duration, incorporate multiple factors but complicate parameter tuning and lack a unified neighbor quality metric. This paper proposes Directional Propagation Capacity Index–GPSR (DPCI-GPSR), integrating neighbor information into a single directional metric capturing propagation capacity. Two enhancements are introduced: (1) an eight-direction DPCI computing a composite propagation capacity index per sector, exchanged via Hello packets, and (2) a trapezoidal link quality function treating 30–200 m as optimal while penalizing edge-zone neighbors. Implemented in NS-3 with SUMO-generated mobility, results across four node densities (30–120 vehicles), five concurrent sender–receiver pairs, and 15 random seeds show DPCI-GPSR achieves 63.08–98.39% PDR, outperforming both W-PAGPSR (52.38–80.14%) and standard GPSR (50.23–66.31%).
Automated Detection of Short-Term Slow Slip Events Using GNSS Data via Change-Point Analysis
(Oxford University Press (OUP), 2025-12-13) Ma, Yiming; Anastasiou, Andreas; Montiel, Fabien
Inferring from the occurrence pattern of slow slip events (SSEs) the probability of triggering a damaging earthquake within the nearby velocity weakening portion of the plate interface is critical for hazard mitigation. Although robust methods exist to detect long-term SSEs consistently and efficiently, detecting short-term SSEs remains a challenge. In this study, we propose a novel statistical approach, called singular spectrum analysis isolate-detect (SSAID), for automatically estimating the start and end times of short-term SSEs in GNSS data. The method recasts the problem of detecting SSEs as that of identifying change-points in a piecewise non-linear signal. This is achieved by obscuring the deviation from piecewise-linearity in the underlying SSE signals using added noise. We verify its effectiveness on a range of synthetic SSE data with different noise levels, and demonstrate its superior performance compared to two existing methods. We illustrate its capability in detecting short-term SSEs in observed GNSS data from 36 stations in southwest Japan via the co-occurrence of non-volcanic tremors, hypothesis tests and fault estimation.
Momentum Vectorized Adaptive DDPG-based PSC Mitigator Design for Hybrid PV-TEG Systems with Auxiliary Battery Participation
(Elsevier BV, 2026-04-01) Zhou, Lei; Yang, Bo; Zhou, Shuai; Li, Hongbiao; Gao, Dengke; Lie, Tek Tjing; Jiang, Lin
Partial shading conditions (PSC) significantly reduce the efficiency of photovoltaic (PV) systems by causing uneven irradiation and mismatched power losses. To address this, this study proposes a novel momentum vectorized adaptive deep deterministic policy gradient (MVA-ADDPG) algorithm for hybrid PV-thermoelectric generation (PV-TEG) systems. The PV-TEG system integrates thermoelectric generators with PV modules to capture waste heat and uses intelligent energy storage coordination to reduce temperature sensitivity and improve system stability. Unlike conventional PV-energy storage systems, which suffer from high energy losses and maintenance costs, the proposed MVA-ADDPG-driven PV-TEG system employs a triple-action heuristic exploration strategy. It combines momentum-accelerated policy gradients with dynamic exploration–exploitation balance. At each step, three candidate actions are evaluated, generated through both heuristic and gradient-based approaches., This enables fine-grained optimization of battery distribution and system performance. Experimental validation on 6 × 4 to 6 × 6 PV-TEG arrays show an average power increase of 26.5% and a mismatch loss reduction of 45.2%. The method achieves fast convergence and maintains reliable performance under varying shading conditions. By recovering waste heat and optimizing cell compensation, the proposed approach extends system lifespan, and enhances economic viability. It offers a robust solution for efficient energy management in complex PV environments.
A Single-Link Propagation-Driven Performance Study of IEEE 802.11be Wi-Fi 7 in Complex Indoor Environments
(MDPI AG, 2026-05-27) Sarkar, Nurul I; Mustafa, Rashid
IEEE 802.11be, commercially known as Wi-Fi 7, extends wireless local area network (WLAN) capability through wider channel bandwidths, higher-order modulation, and tri-band operation. However, realised indoor performance is still strongly affected by radio propagation conditions. This study presents a controlled empirical assessment of Wi-Fi 7 behaviour in a multi-storey university building by examining throughput and received signal strength (RSS) across the 2.4 GHz, 5 GHz, and 6 GHz bands using a single-link measurement setup. Six experimental scenarios were used to examine distance variation, wall penetration, line-of-sight (LOS) obstruction, floor separation, antenna orientation, and microwave interference. The measured RSS values were compared with the free-space, two-ray ground reflection, and log-distance shadowing models using mean absolute error (MAE). Six experimental scenarios were designed to isolate dominant indoor impairments, including distance variation, wall penetration, line-of-sight obstruction, floor separation, antenna orientation, and microwave interference. Measured RSS values were evaluated against free-space, two-ray, and log-distance shadowing models using mean absolute error as the comparison metric. Results show that 2.4 GHz retains greater penetration at lesser capacity, while 6 GHz offers the maximum short-range throughput under clear line-of-sight conditionsbut rapidly deteriorates with structural attenuation. Performance in all bands is greatly diminished by multi-wall blockage and line-of-sight loss. A single propagation model cannot adequately capture the divergence introduced by increasing distance and indoor attenuation, while short-range line-of-sight conditions more closely resemble deterministic predictions in terms of measured RSS alignment. Overall, the results highlight the trade-off between Wi-Fi 7’s capacity and coverage, and provide helpful advice for choosing frequencies, positioning access points, and organizing indoor coverage. The research findings provide insights into the practical deployment of next-generation Wi-Fi in multi-story buildings and residential houses.
Generating Test Cases for Autonomous Vehicles With Controllable Levels of Difficulty
(IEEE, 2026-05-12) Wang, Ziyu; Ma, Jing; Lai, Edmund M-K
Autonomous vehicle (AV) safety validation increasingly relies on scenario-based testing. However, existing approaches to test scenario generation do not provide mechanisms to systematically regulate scenario difficulty. To address this critical limitation, this paper introduces a novel game-theoretic framework for adversarial safety validation. The interaction between the AV-under-test and a strategically obstructing rear vehicle is modelled as a Stackelberg game. The level of adversarial intensity, which reflects the level of difficulty, of a scenario can be controlled by a single tunable parameter called aggressiveness at both the action level and the interaction level. The efficacy of this approach is studied through the highway lane-changing operational design domain. Simulation experiments demonstrate that increasing aggressiveness reduces the success rate of lane-changing and prolongs maneuver duration for the successful attempts. These results confirm that this parameter can effectively and systematically control the difficulty level of test scenarios, providing a valuable tool for rigorous and reproducible AV safety validation.
Comparative Study of Time- and Frequency-Difference Electrical Impedance Tomography for Breast Cancer Detection
(IOP Publishing, 2026-05-21) Gutiérrez López, Marcos; Gutierrez Gnecchi, Jose Antonio; Yang, Wuqiang; Reyes Archundia, Enrique; Rodriguez Herrejon, Javier Alejandro; Robledo Ayala, Alejandro Israel; Garcia, Lorenzo
A multifrequency electrical impedance tomography (EIT) system was evaluated for its ability to detect conductive inclusions simulating carcinomas in breast phantoms, with a comparative analysis of time-difference and frequency-difference reconstruction approaches. The proposed EIT V5 system employs two concentric rings of 16 electrodes to acquire surface voltage measurements at multiple excitation frequencies (50 kHz, 500 kHz, and 1 MHz). Image reconstruction was performed using the Linear Back Projection (LBP) algorithm, and system performance was quantitatively assessed through spatial overlap metrics (intersection over union, IoU, and F1-score), contrast-to-noise ratio (CNR), and confusion-matrix-derived metrics (sensitivity, specificity, and precision). The area under the receiver operating characteristic curve (AUC) was also computed as a pixel-level, threshold-independent separability metric. Experimental phantoms were designed to approximate breast tissue composition, consisting primarily of adipose material with embedded conductive inclusions representing tumors. The results show that frequency-difference EIT consistently outperforms time-difference reconstruction across all evaluated scenarios, achieving higher CNR values (> 2.4) and improved spatial agreement (IoU and F1-score), while time-difference reconstructions exhibit significant variability and reduced contrast at lower frequencies. Although high AUC values (> 0.99) are observed for the frequency-difference approach, these should be interpreted as indicators of conductivity separability within individual reconstructions rather than diagnostic performance. Reconstructions obtained from the lower electrode ring demonstrate increased sensitivity, highlighting the influence of electrode geometry and inclusion proximity on detection performance. Importantly, frequency-difference reconstructions enhance contrast between conductive inclusions and surrounding tissue without requiring a prior baseline measurement. These findings indicate that multifrequency, frequency-difference EIT provides a robust and reliable approach for detecting conductive anomalies in controlled phantom conditions, reducing reconstruction artifacts and improving tissue discrimination. The proposed methodology shows strong potential as an auxiliary tool for early breast cancer detection, intracranial hemorrhage monitoring, and the development of wearable biomedical imaging systems.
Exploring the Potential of Low-Barrier AI Tools for Culturally Responsive STEM Learning: Early Māori and Pacific Learner Insights
(MDPI AG, 2026-05-21) Williams, Toiroa; Nguyen, Minh; Ka'ai, Tania; Vallayil, Manju; Tukimata, Nogiata; Smith-Henderson, Tania
Recent advances in large language models (LLMs) have enabled new forms of software creation through natural-language interaction. However, many AI-assisted coding tools continue to assume familiarity with development environments, programming workflows, and technical conventions, which may limit accessibility for early-stage learners and communities historically underrepresented in digital participation. This challenge is particularly relevant in Aotearoa New Zealand, where Māori and Pacific peoples remain underrepresented across STEM and technology pathways. This paper introduces TechTahi, a browser-based, syntax-free AI-assisted platform designed to support low-barrier digital creation through natural-language prompts and immediate in-browser previews. The study had two aims: to describe the design rationale and workflow of TechTahi and to explore early learner perceptions following initial use of the platform. An exploratory pilot design was employed. Five participants completed a post-use survey after hands-on interaction with TechTahi. Responses were analysed descriptively, with open-ended feedback reviewed for recurring themes. Findings suggested generally positive perceptions of accessibility and ease of use, particularly the ability to create working applications without prior coding knowledge. Participants also identified opportunities for culturally relevant features, including language support and locally meaningful design elements, alongside areas for improvement such as clearer onboarding guidance and reduced information density. These preliminary findings suggest that syntax-free, culturally responsive AI creation tools may offer promising pathways for widening participation in digital learning. Further research with larger and more diverse samples is needed to evaluate longer-term educational impact.
Augmented Reality and Artificial Intelligence for the Assessment and Rehabilitation of Spatial Neglect: A Systematic Review
(SAGE Publications, 2026-05-04) Li, Shaojun; Chong, Benjamin; Mehri-Kakavand, Ghazal; Shi, Catherine; Taylor, Denise; Fowler, Allan; Billinghurst, Mark; Harvey, Monika; Wang, Alan
Background and Purpose: Augmented reality (AR) and artificial intelligence (AI) have been applied to the assessment and rehabilitation of post-stroke spatial neglect (SN). This study aims to evaluate the feasibility, effectiveness, degree of personalization, and ecological validity of AR, AI, and hybrid methods for SN assessment and rehabilitation. Methods: PubMed, Scopus, Web of Science, Embase, CINAHL, IEEE Xplore, and the ACM Digital Library were searched up to August 2025. Two reviewers independently screened articles, extracted data, and assessed risk of bias and outcome-level certainty. Results: Of 268 screened studies published between 2000 and 2025, 15 met the inclusion criteria, including 11 assessment studies (8 AI, 1 AR, and 2 hybrid) and 4 AR rehabilitation studies, involving 567 participants. AI assessment methods demonstrated high diagnostic accuracy (area under the curve (AUC) up to 0.95), and 1 AR assessment showed strong diagnostic accuracy (AUC = 0.89). Four AR rehabilitation studies reported acceptable feasibility, with 1 randomized controlled trial (RCT) showing improvements in several neglect outcomes. Ecological validity and personalization were generally very low, and the overall certainty of evidence ranged from low to very low. Conclusion: Current evidence for AR and AI SN assessment and rehabilitation methods remains insufficient to determine their feasibility, effectiveness, ecological validity, and degree of personalization, largely due to small sample sizes, methodological heterogeneity, and the limited number of RCTs. Future research should focus on developing standardized, scalable frameworks that integrate AR with adaptive AI models, and multicenter RCTs are required to confirm clinical efficacy and long-term functional outcomes
Advancements and Challenges in Blood Pressure Monitoring Using Pulse Wave Propagation: A Comprehensive Review and ISO 81060-2 Based Statistical Analysis
(Springer Science and Business Media LLC, 2026-05-07) Yu, Yang; Lowe, Andrew
Cardiovascular diseases, particularly hypertension, remain a major global health burden, highlighting the need for accurate and accessible blood pressure (BP) monitoring. Cuffless BP measurement (BPM) based on pulse wave propagation methods (PWPM), including pulse arrival time (PAT), pulse transit time (PTT), and pulse wave velocity (PWV), has attracted increasing research interest. This review comprises two components. First, a narrative review of studies published up to June 2025 examines sensing technologies, mathematical models, and validation protocols used in PWPM-based BPM. Second, a statistical re-evaluation of 22 studies published between 2015 and 2025 was conducted using the Credence of Device Acceptability (CDA) and the Probability of Tolerable Error (PTE), grounded in the statistical principles of ISO 81060-2. Accuracy varied widely across physiological conditions, sensing technologies, and study designs, with no single approach demonstrating consistent superiority. The re-evaluation provided a more stringent assessment of performance: only five studies achieved CDA values exceeding 0.95 for both systolic and diastolic BP. Overall, diastolic BP estimation demonstrated superior accuracy compared with systolic BP. Incorporating physiological indices such as arterial compliance and sympathetic activity may improve the robustness and accuracy of BP estimation models. While machine learning shows promise for enhanced feature extraction, calibration tolerance and real-world reliability remain critical challenges. Importantly, the evaluation and development of cuffless BPM technologies should align with validation standards appropriate to the intended application. We recommend that future early-stage studies apply the CDA and PTE framework as supportive accuracy metrics to better assess methodological performance and inform device development and validation.
A Novel Transient Thermal Analysis of Direct Steam Generation External Receivers in Solar Power Tower Plants Under Atmospheric Conditions Fluctuations
(ASME International, 2026-04-27) Al-Sarraf, Hayder; Alhusseny, Ahmed; Zamora, Ramon
Solar power tower plants are pioneer candidates for electric power generation; hence, such plants concentrate solar thermal power to heat the working medium used in the power cycles. However, atmospheric effects and cloud cover cause spatial and temporal fluctuations in solar thermal power during the day. Thus, evaluating the net power acquired by solar receiver tubes as a function of time and location is of high interest. A thorough dynamic thermal analysis procedure is developed in this research and examined under realistic weather conditions to demonstrate its potential for managing complex computations thoroughly and cost-effectively. Three operational scenarios regarding their impact on the steam bulk temperature, productivity, and enthalpy are discussed. Among them, Scenario #3 outperforms in terms of net productivity due to the lower overall makeup required throughout the day, where the receiver can meet 93.61% of the plant steam demand when standalone, compared to 90.44% and 89.06% when Scenarios #1 and #2 are followed. From a safe operation point of view, the wall temperature of the superheater tubes on the north, east, and west sides exceeds the maximum allowable limit. To address this issue, a mass flow interchange approach with optimal circulation factors between the opposing sides is proposed using a temperature control valve. It was found that the uneven distribution of steam fed into the superheater sides not only guarantees the receiver's safety but also slightly reduces the total makeup required while improving the excess energy available.
Human-Centered XR Integration for STEM Education in New Zealand: A Systematic Review and Implementation Framework
(MDPI AG, 2026-05-20) Iqbal, Muhammad Faisal Buland; Tran, Kien TP; Yan, Wei Qi; Abraham, Hazel; Nguyen, Minh
This systematic review comprehensively explores the integration of Extended Reality (XR) technologies, comprising Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR), into New Zealand’s STEM education framework. In alignment with PRISMA 2020 guidelines, we systematically analyzed 127 peer-reviewed studies from the Web of Science (n = 48), Scopus (n = 57), and Dimensions (n = 22) and incorporated 15 grey literature sources, resulting in 142 studies included in the review. Our meta-analysis found substantial improvements in student conceptual understanding from XR-enhanced STEM modules. Specifically, we observed an average increase of 23.4% when compared to traditional instructional methods (95 percent Confidence Interval: 18.7 to 28.1 percent, p < 0.001). These gains were especially prominent in interactive learning environments where immersive XR applications supported deeper engagement and the visualization of abstract STEM concepts. The qualitative synthesis highlighted several key barriers that limit effective XR integration. These include technological infrastructure gaps reported in 68 percent of reviewed studies, a critical need for educator training cited by 82 percent of studies, and curriculum alignment issues present in 57 percent of cases. Methodological quality was assessed using the Mixed Methods Appraisal Tool (MMAT) 2018, and the qualitative component employed a deductive thematic coding approach with inter-coder reliability verification. Successful institutional implementations were also identified. At Auckland University of Technology, XR-supported courses produced a 67 percent increase in student engagement, while Wellington High School achieved a 41 percent reduction in STEM achievement gaps through targeted XR interventions. Based on the evidence, we propose a four-phase implementation framework that addresses the technological, pedagogical, and policy requirements for sustainable XR adoption. These findings highlight the role of immersive technologies in supporting human-centered digital transformation and future skills development in the transition to Industry 5.0. The review contributes evidence-based insights that support the transition from technology-driven approaches associated with Industry 4.0 to the human-centered, socially oriented priorities of Industry 5.0. It also identifies critical research gaps, particularly in long-term learning outcomes and the integration of Mātauranga Māori within XR-enabled STEM environments.
Smart-Contract-based Automation for OF-RAN Processes: A Federated Learning Use-Case
(MDPI AG, 2022-09-13) Jijin, Jofina; Seet, Boon-Chong; Chong, Peter Han Joo
The opportunistic fog radio access network (OF-RAN) expands its offloading computation capacity on-demand by establishing virtual fog access points (v-FAPs), comprising user devices with idle resources recruited opportunistically to execute the offloaded tasks in a distributed manner. OF-RAN is attractive for providing computation offloading services to resource-limited Internet-of-Things (IoT) devices from vertical industrial applications such as smart transportation, tourism, mobile healthcare, and public safety. However, the current OF-RAN design is lacking a trusted and distributed mechanism for automating its processes such as v-FAP formation and service execution. Motivated by the recent emergence of blockchain, with smart contracts as an enabler of trusted and distributed systems, we propose an automated mechanism for OF-RAN processes using smart contracts. To demonstrate how our smart-contract-based automation for OF-RAN could apply in real life, a federated deep learning (DL) use-case where a resource-limited client offloads the resource-intensive training of its DL model to a v-FAP is implemented and evaluated. The results validate the DL and blockchain performances of the proposed smart-contract-enabled OF-RAN. The appropriate setting of process parameters to meet the often competing requirements is also demonstrated.

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