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 1856

Integrating Energy Efficiency, Water Efficiency and Indoor Environmental Quality Towards Advancing Sustainable Building Designs and Practices in New Zealand: Stakeholders’ Perspectives
(Emerald, 2026-04-08) Poorisat, Tharaya; Aigwi, Itohan Esther; Doan, Dat Tien; GhaffarianHoseini, Ali
Purpose Sustainable building designs and practices (SBDPs) are increasingly recognised worldwide for their potential to enhance environmental sustainability, reduce resource consumption and improve occupant well-being. In New Zealand, however, the integration of energy efficiency (EE), water efficiency (WE) and indoor environmental quality (IEQ) remains a critical but underexplored area. This study aims to investigate the combined benefits, challenges and recommendations associated with integrating EE, WE and IEQ in advancing SBDPs within the New Zealand context. Design/methodology/approach Semi-structured interviews were undertaken with 43 experts engaged in sustainable building initiatives. Thematic analysis was used to explore stakeholder perspectives on the benefits, challenges and recommendations associated with EE, WE and IEQ integration. Findings The findings reveal that EE is widely regarded as a fundamental driver of sustainable buildings, but cost concerns, weak regulations, and market-driven priorities constrain its implementation. WE is frequently overlooked, despite its potential to support water conservation, owing to perceptions of resource abundance, limited policy support and financial barriers. IEQ is increasingly valued for its role in promoting occupant health, comfort and productivity, but regulatory and awareness gaps remain significant. Originality/value This study provides one of the first holistic assessments of EE, WE and IEQ integration in SBDPs in New Zealand. By framing New Zealand as a testbed for countries with high renewable potential, abundant water resources and weak regulatory enforcement, the research advances academic discourse while delivering actionable insights for policymakers, practitioners and investors to promote meaningful sustainability transitions in the built environment.
Tertiary Student Experiences Connecting Context and Mathematics While Mathematically Modelling
(Springer Nature Switzerland, 2025-08-10) Spooner, Kerri
Connecting mathematics and contexts is fundamental when mathematically modelling. However, connecting mathematics and contexts appears unfamiliar for mathematics students. To inform teaching practices, this study looked at: What are the student experiences for connecting mathematics and contexts while participating in mathematical modelling? Tertiary student mathematical modelling experiences with modelling activities from three different New Zealand university modelling courses were looked at. Data was collected through semi-structured interviews with the students. Reflective thematic analysis was used to identify themes in students’ experiences. The results showed connecting mathematics and contexts was: new for students; can make mathematics more relatable; a different experience for different students depending on their backgrounds.
Fully Automated, Deep Learning, Cardiac CT-based Multimodal Network for Cardiovascular Risk Stratification in High-Risk Perioperative Patients
(Oxford University Press, 2026-03-04) Lu, Juan; Huangfu, Gavin; Ihdayhid, Abdul; Bennamoun, Mohammed; Konstantopoulos, John; Kwok, Simon; Niu, Kai; Liu, Yanbin; Figtree, Gemma A; Chan, Matthew TV; Butler, Craig R; Tandon, Vikas; Nagele, Peter; Woodard, Pamela K; Mrkobrada, Marko; Szczeklik, Wojciech; Abdul Aziz, Yang Faridah; Biccard, Bruce M; Devereaux, Philip James; Sheth, Tej; Williams, Michelle C; Newby, David E; Chow, Benjamin JW; Dwivedi, Girish
AIMS: Major adverse cardiac events (MACE) significantly impact perioperative morbidity and mortality. We aimed to develop a fully automated multimodal deep learning (DL) system integrating patient demographics, comorbidities, and coronary computed tomography angiography (CCTA) findings to optimize risk prediction. METHODS AND RESULTS: We included 639 patients undergoing CCTA as part of perioperative risk assessment for elective non-cardiac surgery. Convolutional neural networks automatically identified coronary artery disease reporting and data system (CAD-RADS) scores and segmented the left ventricle, aorta, and heart. These imaging features were combined with patient demographics and comorbidities to predict MACE risk. We evaluated the performance of our multimodal model against the revised cardiac risk index (RCRI) using gradient boosting decision tree modelling and area under the receiver operating characteristic (AUROC) curves. Among 639 patients (mean age 70 ± 9 years, 56% males, median RCRI 1), 61% underwent orthopaedic surgery, 27% vascular surgery and the rest abdominal/pelvic or spine surgery. 45 patients experienced MACE within 30 days. Automated CAD-RADS (AUROC = 0.69) demonstrated comparable performance to human analysis (AUROC = 0.67, P = 0.77). The multimodal DL system (AUROC = 0.82) outperformed CAD-RADS (delta-AUROC = 0.13, CI: 0.02, 0.26, P = 0.02), and RCRI (delta-AUROC =0.22, CI: 0.05, 0.46; P = 0.001) in predicting MACE and demonstrated robust sensitivity (83%) and specificity (79%). CONCLUSION: Our multimodal system built using automated CAD-RADS, anatomical segmentations and patient demographics outperforms both human expert and automated CAD-RADS for MACE prediction. This approach has the potential to enhance patient outcomes by leveraging the synergy between automated imaging and clinical data.
Scrum Master Competencies: A Content Analysis of Job Postings
(Elsevier, 2026-03-24) Mehta, Nitin; Tan, Felix B
This study explores the competencies required for Scrum Masters by analyzing 51 job postings from the United States, the United Kingdom, New Zealand, and Australia. The findings reveal a growing demand for Scrum Masters with a diverse skill set, including leadership, coaching, communication, and problem-solving, alongside Agile expertise. The results align with frameworks such as the Scrum Guide and SAFe, emphasizing the importance of practical experience and the Scrum Master’s role in driving organizational transformation and promoting Agile maturity. These insights highlight the evolving expectations for Scrum Masters in modern Agile environments.
AVAESA: Adaptive VAE With Self-attention and Learnable Signal Processing for Robust Radar-based Heart Rate Estimation
(Elsevier BV, 2026-03-26) Shirazi, Mohammad Hossein; Yongchareon, Sira; Singh, Anuradha; Ma, Jing
Non-contact heart rate monitoring using radar sensors offers significant advantages for healthcare and automotive applications by preserving privacy while enabling continuous physiological assessment. Current Variational Autoencoder (VAE) approaches for radar-based vital sign monitoring, while superior to traditional neural networks, suffer from fixed preprocessing assumptions and inadequate temporal modeling that limit their generalization across diverse measurement conditions. This study introduces AVAESA (Adaptive VAE with Self-Attention and Learnable Signal Processing), a novel architecture that addresses these limitations through three key innovations: dual-stream in-phase/quadrature signal processing that preserves critical phase relationships, multi-head self-attention mechanisms for enhanced temporal dependency modeling, and adaptive signal preprocessing with learnable parameters that derive frequency bands and processing weights directly from input signal characteristics. The framework was evaluated on 1920 measurements from 10 participants across 48 measurement scenarios (4 distances  ×  3 angles  ×  4 orientations), assessing cross-scenario robustness under varied measurement conditions, with Polar H10 chest strap ground truth validation. Comprehensive comparison against multiple architectures (CNN, LSTM, Bi-LSTM, TCN, VAE) with statistical significance testing demonstrates substantial performance improvements, with mean absolute error reductions ranging from 17.3% under optimal conditions to 62.6% under challenging cross-scenario generalization scenarios. AVAESA maintains high accuracy (correlation coefficient  >  0.86, R² >  0.84) even under challenging measurement conditions where baseline approaches exhibit degraded performance, demonstrating potential for contactless cardiac monitoring systems across diverse measurement environments through improved cross-scenario robustness.
Computational Investigation of the Cooling and Heating Effect in Vortex Tube
(ASME International, 2026-03-31) Duan, Dingli; Guan, Shuaibing; Qiao, Yanfeng; Wang, Jay
Vortex tubes exhibit a unique temperature separation phenomenon, leading to their widespread application in refrigeration and heating. Using the exact flow and temperature field simulation, this study examined the cooling effect and heating effect under different cold mass fractions, inlet pressure and diameter ratios. The cooling effect shows a trend of first increasing and then decreasing with the increase of the cold mass fractions while the heating effect gradually increases. Actually, the cooling and heating effect are suppressed by backflow, it is difficult for the working fluid to flow out of the cold end leading to the backflow at the cold end is severe at low cold mass fractions, and the reverse flow boundary exceeds the control valve causing the backflow at the hot exit is enhanced at high cold mass fractions. In order to get excellent cooling effect and heating effect of the vortex tube, the inlet pressure should be increased while reducing the diameter ratio of the vortex tube, and the cold mass fractions should be controlled within a reasonable range (CMF=0.3∼0.7).
eXCube2: Explainable Brain-inspired Spiking Neural Network Framework for Emotion Recognition From Audio, Visual and Multimodal Audio–Visual Data
(MDPI AG, 2026-03-14) Kasabov, NK; Yang, A; Wang, Z; Abouhassan, I; Kassabova, A; Lappas, T
This paper introduces a biomimetic framework and novel brain-inspired AI (BIAI) models based on spiking neural networks (SNNs) for emotional state recognition from audio (speech), visual (face), and integrated multimodal audio–visual data. The developed framework, named eXCube2, uses a three-dimensional SNN architecture NeuCube that is spatially structured according to a human brain template. The BIAI models developed in eXCube2 are trainable on spatio- and spectro-temporal data using brain-inspired learning rules. Such models are explainable in terms of revealing patterns in data and are adaptable to new data. The eXCube2 models are implemented as software systems and tested on speech and video data of subjects expressing emotional states. The use of a brain template for the SNN structure enables brain-inspired tonotopic and stereo mapping of audio inputs, topographic mapping of visual data, and the combined use of both modalities. This novel approach brings AI-based emotional state recognition closer to human perception, provides a better explainability and adaptability than existing AI systems. It also results in a higher or competitive accuracy, even though this was not the main goal here. This is demonstrated through experiments on benchmark datasets, achieving classification accuracy above 80% on single-modality data and 88.9% when multimodal audio–visual data are used, and a “don’t know” output is introduced. The paper further discusses possible applications of the proposed eXCube2 framework to other audio, visual, and audio–visual data for solving challenging problems, such as recognizing emotional states of people from different origins; brain state diagnosis (e.g., Parkinson’s disease, Alzheimer’s disease, ADHD, dementia); measuring response to treatment over time; evaluating satisfaction responses from online clients; cognitive robotics; human–robot interaction; chatbots; and interactive computer games. The SNN-based implementation of BIAI also enables the use of neuromorphic chips and platforms, leading to reduced power consumption, smaller device size, higher performance accuracy, and improved adaptability and explainability. This research shows a step toward building brain-inspired AI systems.
Data-Driven Parameter Identification of Synchronous Generators: A Three-Stage Framework with State Consistency and Grid Decoupling
(MDPI AG, 2026-03-24) Peykarporsan, Rasool; Govinda Waduge, Tharuka; Lie, Tek Tjing; Stommel, Martin
As modern power systems grow increasingly complex, there is a pressing need for stability analysis methods capable of handling nonlinear dynamics while providing physically meaningful and reliable stability indices. Port-Hamiltonian (PH) frameworks have emerged as strong candidates in this regard, offering inherently stable formulations, energy-consistent representations, and modular plug-and-play scalability. However, the practical deployment of PH-based stability analysis remains hindered by the absence of reliable, high-fidelity parameter identification methods that rely on sensor measurements to capture system dynamics while remaining compatible with PH model structures. This paper addresses that gap by proposing a comprehensive three-stage data-driven identification framework for PH modeling of synchronous generators—the central dynamic component of any power system. While the IEEE Standard 115 provides established procedures for transient parameter identification, it exhibits fundamental limitations when applied to PH modeling, including single-scenario identifiability constraints, noise-sensitive derivative-based formulations that amplify sensor measurement errors, and the inability to decouple generator-internal damping from grid contributions. The proposed framework resolves these limitations through multi-scenario excitation using sensor-acquired voltage and current signals, derivative-free state consistency optimization, and physics-based regularization that enforces PH structure preservation. Complete identification of eight key parameters (H, D, Xd, Xq, Xd′, Xq′, Tdo′, Tqo′) is achieved with errors ranging from 1.26% to 9.10%, and validation confirms RMS rotor angle errors below 1.2° and speed errors below 0.15%, demonstrating suitability for transient stability analysis, passivity-based control design, and oscillation damping assessment.
Cyber-Physical Anomaly Detection a Deep Adversarial Fusion of Sensor and Network Data
(Springer Science and Business Media LLC, 2026-03-30) Pinto, Andrea; Herrera, Luis-Carlos; Donoso, Yezid; Gutierrez, Jairo A
In critical infrastructure, the convergence of physical systems with digital networks forms complex Cyber-Physical Systems (CPS), that are vulnerable to threats compromising both data and physical operations. Traditional security systems, often focused solely on network traffic, create a significant security gap by neglecting the rich contextual data provided by physical sensors. To address this issue, the paper introduces a novel unsupervised multimodal framework that synthesizes data from these dual sources for holistic anomaly detection. The proposed architecture combines pre-trained Variational Autoencoder-Long Short-Term Memory (VAE-LSTM) networks to model temporal dependencies with a dual cross-attention mechanism for deep fusion of latent representations. To enhance the detection of subtle, low-observability threats, the model is further regularized through adversarial training using a discriminator that distinguishes between original and reconstructed data. Evaluated on the comprehensive SWAT dataset, the model successfully identifies 24 out of 26 relevant attack scenarios using 10-second time sequences and achieves an Area Under the Curve (AUC) of 0.87, outperforming unimodal benchmarks. This work validates the critical importance of deep data fusion and presents a more resilient, context-aware defense mechanism for modern CPS.
Benchmarking Nonlinear Controllers for a Quad Active Bridge Resonant Converter Supplying Pulsed Power Loads in DC Microgrids
(Elsevier BV, 2026-05) Nduwamungu, Aphrodis; Lie, Tek Tjing; Nair, Nirmal KC; Lestas, Ionis
This paper investigates advanced nonlinear control strategies for a Quad Active Bridge (QAB) resonant converter supplying pulsed power loads (PPLs) in DC microgrids, where fast load transients and inter-port coupling challenge voltage stability. Three controllers are designed and evaluated for regulating the 48 V ports 3 and 4 under a PPL step from 100 W to 200 W : ASSOSMC, IHOSMC, and CMPC–ISMC. In terms of voltage regulation, ASSOSMC achieves the smallest peak deviations ( Δ V 3 = 0 . 5 V , Δ V 4 = 0 . 3 V ), whereas IHOSMC and CMPC–ISMC exhibit larger voltage dips of 3 . 0 / 2 . 8 V and 3 . 5 / 4 . 2 V at ports 3/4, respectively. Overshoot is kept below 1.531% (ASSOSMC) and limited to 0.667% (IHOSMC and CMPC–ISMC), with fast settling times of 3 . 6 μ s (ASSOSMC and CMPC–ISMC) and 4 . 7 μ s (IHOSMC). Energy efficiency is evaluated from steady-state averaged port powers over a common interval ( t = 2 . 1166 – 2 . 8610 s ): CMPC–ISMC achieves the highest overall efficiency ( η tot ≈ 90 . 775 % ), followed by ASSOSMC ( η tot ≈ 90 . 724 % ) and IHOSMC ( η tot ≈ 89 . 055 % ). Hardware-in-the-loop validation on an OPAL-RT platform confirms the effectiveness of the proposed controllers for stabilizing QAB port voltages under PPL disturbances, and highlights CMPC–ISMC as a balanced solution combining high efficiency with reduced current stress on the regulated ports.
LIGO Core-collapse Supernova Detection Using Convolutional Neural Networks
(MDPI AG, 2026-03-10) Pan, Zhicheng; Zahraoui, El Mehdi; Maturana-Russel, Patricio; Cabrera-Guerrero, Guillermo
Core-collapse supernovae (CCSNe) remain a critical focus in the search for gravitational waves in modern astronomy. Their detection and subsequent analysis will enhance our understanding of the explosion mechanisms in massive stars. This paper investigates the use of convolutional neural networks (CNN) to enhance the detection of gravitational waves originating from CCSNe. We employ two time–frequency analysis techniques to generate spectrograms (training data): short-time Fourier transform (STFT) and Q-transform (QT). Two CNNs were trained independently on sets of spectrogram images of simulated CCSNe signals and advanced LIGO noise. The CNNs detect CCSNe signals based on their time–frequency representation. Both CNNs achieve a near 100% true positive rate for CCSNe GW events with a signal-to-noise ratio greater than 0.5 in our test set. Nevertheless, the CNN trained on the STFT spectrograms outperforms the one based on the Q-transform for SNRs below 0.5.
Research on a Precision Counting Method and Web Deployment for Natural-form Bothriochloa ischaemum Spikes and Seeds Based on Object Detection
(MDPI AG, 2026-03-27) Zhao, Huamin; Zhang, Yongzhuo; Zheng, Yabo; Zeng, Erkang; Jiang, Linjun; Yan, Weiqi; Xia, Fangshan; Xu, Defang
Bothriochloa ischaemum is a key forage species with strong grazing tolerance and high nutritional value, making precise quantification of spike and seed traits essential for germplasm evaluation and yield prediction. However, the compact architecture and minute seed size in natural field conditions render manual counting inefficient and labor-intensive. To address this limitation, this study presents a non-destructive and automated quantification framework integrating advanced object detection and regression analysis for accurate in situ estimation of spikes and seed numbers. To further address the challenges of dense spike detection caused by occlusion and small object sizes, this study developed a modified model named YOLOv12-DAN by integrating DySample dynamic upsampling, ASFF feature fusion, and NWD loss, which achieved a mean average precision (mAP) of 91.6%. Meanwhile, for the detection of dense kernels on compact spikes, an improved YOLOv12 architecture incorporating an Explicit Visual Center (EVC) module was proposed to enhance multi-scale feature representation. The optimized model attained a bounding box precision of 96.5%, a recall rate of 86.4%, an mAP50 of 94.3%, and an mAP50-95 of 73.9%. Furthermore, a univariate linear regression model based on 132 spike samples verified the reliable consistency between the predicted and actual seed counts, with a mean absolute error (MAE) of 6.30, a mean absolute percentage error (MAPE) of 9.35, and an R-squared (R2) value of 0.808. Finally, the model was deployed through a lightweight end-to-end web application, enabling real-time field operation and promoting its applicability in breeding programs and agronomic decision-making. This study provides a robust technical pathway for automated phenotyping and precision forage improvement.
The Scaffolded AI Literacy (SAIL) Framework: Results of a Delphi Study for Equitable AI Literacy Framework Design in Education
(Elsevier BV, 2026-03-24) MacCallum, Kathryn; Parsons, David; Mohaghegh, Mahsa
Developments in AI technologies and their increasing use in education have prompted ongoing interest in the development of generally applicable AI literacy frameworks to ensure equity of access to knowledge, skills, and understanding. Researchers have investigated the components of AI literacy, how they can be structured, and how they can be developed in educators and learners. Despite much work in this area, and the opportunities and challenges presented by generative AI technologies, most recent frameworks have been confined to those that simply aggregate older ideas from the literature or those that focus on non-generalisable contexts. Few existing frameworks provide novel perspectives on generic approaches to AI literacy that also support equitable, scaffolded competency development for all learners, regardless of context. In contrast, this article reports on a Delphi study that led to the creation of the Scaffolded AI Literacy (SAIL) framework, which is broadly applicable across contexts but also accessible enough to be easily assimilated into the curriculum. Unlike many other frameworks, it provides a scaffolded pathway through competency levels that can be applied across all ages and stages of education and helps to address second- and third-level digital divides. This article details how the Delphi study unfolded, the key decisions that were made during the process, the resulting framework, and how it may contribute to equitable access to AI literacies.
Multi-head Noise Regression for Single-channel EEG: Estimating Ocular and Muscle Contamination to Guide Artifact Removal
(IOP Publishing, 2026-03-18) Shaikh, Usman Qamar; Kalra, Anubha; Lowe, Andrew; Niazi, Imran Khan
EEG is often contaminated by ocular (EOG) and muscle (EMG) artifacts, yet many pipelines apply uniform denoising, risking distortion of clean neural activity. We propose a two-head, single-channel regressor that estimates EOG and EMG noise-to-signal ratio (NSR, dB) from short segments and test whether it can guide selective artifact reduction, including downstream BCI decoding. Approach. Using EEGdenoiseNet clean EEG and artifact exemplars, we synthesised 2-s single-channel mixtures with known EOG/EMG NSR spanning -10 to +10 dB and trained several model families to jointly regress both NSRs. Generalisation was evaluated on an independent eyeblink dataset via agreement with regression-based ocular-reference topographies, and in two applications: (i) gating stationary wavelet blink removal on a P3 ERP dataset and (ii) gating the same denoiser on a 55-subject RSVP P300 speller dataset (FP1/FP2). Main results. A dilated temporal convolutional network (TCN) performed best (EOG: MAE ≈ 1.8 dB, R² ≈ 0.82; EMG: MAE ≈ 1.0 dB, R² ≈ 0.94) with low bias across NSR. The EOG head recovered blink topographies (median spatial correlation ≈ 0.91). On the P3 dataset, indiscriminate wavelet denoising reduced significant ERP channels, whereas TCN-guided gating preserved 22-23 of 24 while processing ~9-20% of segments. On the speller dataset, denoising all epochs reduced decoding, while selective denoising improved AUC (θ = 9 dB: ΔAUC = 0.327, p = 0.0040) while denoising 12.45 ± 9.29% of test segments. Significance. Multi-head noise regression provides interpretable, continuous ocular and muscle contamination estimates that can act as control signals for conservative, noise-aware artifact handling under constrained-lead conditions. .
Closing the Plastic Budget Equation in Fluvial Systems: A Review of Monitoring Methods
(Elsevier BV, 2026-03-06) Todorova, A; Niven, RK; Kramer, M
As plastic waste increasingly accumulates in aquatic environments, recent efforts have focused on quantifying the contribution of riverine sources to the ocean's plastic pollution. Accurate monitoring of fluvial plastics is challenging because plastics are transported and stored in different layers and compartments. Some processes, such as surface transport, can be measured relatively easily, while others, including bed load transport or storage in biota, remain difficult to assess. We start with a generalized formulation of the plastic budget equation, where we identify several transport and storage terms. Subsequently, we review common monitoring methods for plastics transported as surface load, suspended load, and bed load, as well as for plastics stored in sediments, floodplains, and fluvial biota. Overall, we hope to contribute to a better understanding and monitoring of fluvial plastic dynamics, with the aim to support the development of viable strategies regarding plastic pollution management and remediation.
Grid Efficiency and Power Quality Improvements in Rooftop Solar EV Charging Stations Using Smart Battery Management and Advanced DC-to-DC Converters
(MDPI AG, 2026-03-11) Vaidya, Shanikumar; Prasad, Krishnamachar; Kilby, Jeff
The adoption of electric vehicles (EVs) is a promising strategy for reducing emissions and promoting sustainable mobility. The increasing adoption of EVs has created a demand for efficient and sustainable charging infrastructure. The integration of rooftop solar-powered EV charging stations into distribution networks is a promising solution for reducing carbon emissions and improving grid efficiency. This integration also introduces challenges, such as power quality issues, grid instability, and the impact of environmental factors on solar generation. This study proposes a novel system that integrates a smart control algorithm for a central battery management system (CBMS) with advanced bidirectional DC-DC converters for optimised power distribution. Unlike existing systems that focus on individual components, this study combines real-time environmental monitoring with adaptive power management algorithms to handle variations in generation owing to solar irradiance, temperature, and shading, and ensure maximum power harvesting. This study also presents the role of the DC-to-DC converter integrated with a smart charging control and CBMS in smart grid-enabled EV charging station. The proposed system was validated using MATLAB 2025b Simulink simulations. This study demonstrates an improvement in overall grid stability and highlights the potential of DC-DC converter technologies for smart grid applications and decarbonisation efforts.
An Innovative Pedagogical Strategy for Teaching and Assessing Critical Thinking in Mathematics
(Informa UK Limited, 2026-03-18) Klymchuk, Sergiy; Sangwin, Chris
This article describes and promotes an innovative pedagogical strategy for teaching and assessing critical thinking in mathematics. It can be applied at secondary and tertiary level. The idea is to use mathematics questions that are deliberately designed to mislead the solver and direct to an incorrect solution. We call such questions provocative questions. The intention is to encourage students to critically analyse a mathematical question first before applying certain techniques or software to solve it. That is to enhance a habit to question the question, pay attention to conditions and constraints, recognise mistakes and don’t take anything for granted which is an essential part of a mathematical way of thinking. Several examples demonstrate the idea in the article. Attitudes of secondary school mathematics teachers and university lecturers towards the suggested pedagogical strategy are presented and compared. Implications for teaching practice are also discussed.
A Simplified Numerical Procedure for the Characterization of an Ionic Liquid Meniscus With Evaporation
(Springer Science and Business Media LLC, 2026-02-26) Gooch, F; Cater, J; Aw, K; Sharma, R
Ionic liquid pure-ion electrospray propulsion may be a pivotal technology for micro-satellite propulsion. Extensive modeling is required to understand the underlying physical interactions. However, combining the coupled electric fields, transport mechanisms, and heating effects in a generalized numerical model remains challenging. An investigation conducted by Coffman allowed for insight into the dominant physical processes. This model was further refined, and a second implementation substantially improved computational performance and numerical stability. Unfortunately, these studies still struggled with the significant computational cost. Drawing from the results in Coffman’s previous work and integrating the later stability enhancements, the present work achieves a more robust and computationally efficient implementation. Here, the increased numerical stability of the revised model permits a larger range of parameter values to be used, and allows for data collection at practical capillary sizes, opening the door to experimental validation. Using the revised model, the thermodynamic properties of the ionic liquid are varied to explore the effect of diffusivity on the meniscus, which indicates increased numerical stability for liquids with higher diffusivity.
Energy Management in Offshore Islanded Hydrogen DC Microgrids: A Cost and Electrolyzer Efficiency Optimization Approach
(Elsevier BV, 2026-03-04) Indrajith, Bawantha; Gunawardane, Kosala; Hossain, Md Alamgir; Li, Li; Nicholson, Robert; Zamora, Ramon; Preece, Mark Anthony
In the real-time energy management of offshore islanded microgrids, determining the optimal operating points of storage systems, particularly in hydrogen-based storage, poses significant challenges. These arise from the stochastic nature of offshore Renewable Energy Sources (RES), variable power demand, and the volatility of hydrogen systems. To address these, a novel objective function has been developed that integrates electrolyzer system efficiency into the Energy Management Strategy (EMS) of a DC microgrid. Unlike most existing literature, which considers electrolyzer efficiency as a constant, this work treats efficiency as a dynamic variable that depends on operating current, temperature, and pressure. The behavior of the electrolyzer efficiency with respect to these parameters is modeled, verified, and subsequently incorporated into the EMS. A MINLP based EMS is developed to implement the proposed formulation, and its effectiveness is validated by demonstrating optimal microgrid performance under the above scenario. Notably, the impact of optimal temperature and pressure control is evidenced by electrolyzer efficiency improvements of 1.7% and 3.1% in the efficiency-focused and multi-objective scenarios, respectively. The proposed EMS is evaluated against a rule-based and heuristic (PSO) methods. In the cost-based case, the developed method shows 36.5% and 0.04% cost reductions relative to the rule-based approaches, and a 0.39% reduction relative to PSO. In the efficiency-based scheme, it attains 0.10% efficiency gain over PSO for the optimal temperature-pressure method, and in the multi-objective case, it delivers 7.40% efficiency gain versus PSO.
Reformulated Predictive Torque and Flux Control With a Full-order Adaptive Observer and Accurate Discrete-time Models for Sensorless Induction Machine Drives
(Nature Portfolio, 2026-03-09) Herrera-Hernández, Ramón; Reusser, Carlos; Carvajal, Rodrigo; Zamora, Ramon
In this paper, we present a reformulation of both the predictive torque and flux control (PTC) scheme and the full-order adaptive observer (FAO) for induction machine drives. The proposed approach is based on a state-space representation expressed exclusively in terms of stator current and stator flux linkage, simplifying the observer structure and removing the explicit dependence on rotor flux variables found in conventional sensorless formulations. This representation is consistently applied within both the FAO and PTC frameworks, and second- and higher-order discrete-time models are derived using Taylor- and Runge-Kutta-based methods to enhance numerical accuracy and dynamic performance. The resulting FAO-PTC scheme is validated through Hardware-in-the-Loop simulations, demonstrating steady-state performance comparable to conventional designs, faster transient response, improved dynamic behaviour, and a reduced state-space order, albeit with slightly higher computational cost. Notably, simply employing a more accurate observer substantially enhances the performance of the sensorless scheme. Among the evaluated discretization strategies, the Taylor-based model provides the highest steady-state accuracy and fastest convergence, with only a modest increase in torque ripple. Overall, the proposed reformulated FAO-PTC framework achieves a balanced trade-off between accuracy, implementation simplicity, and computational efficiency for real-time sensorless induction machine drives.

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