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 1887

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.
An Optimization Framework for Data Collection in Software Defined Vehicular Networks
(MDPI AG, 2023-02-01) Wijesekara, Patikiri Arachchige Don Shehan Nilmantha; Sudheera, Kalupahana Liyanage Kushan; Sandamali, Gammana Guruge Nadeesha; Chong, Peter Han Joo
A Software Defined Vehicular Network (SDVN) is a new paradigm that enhances programmability and flexibility in Vehicular Adhoc Networks (VANETs). There exist different architectures for SDVNs based on the degree of control of the control plane. However, in vehicular communication literature, we find that there is no proper mechanism to collect data. Therefore, we propose a novel data collection methodology for the hybrid SDVN architecture by modeling it as an Integer Quadratic Programming (IQP) problem. The IQP model optimally selects broadcasting nodes and agent (unicasting) nodes from a given vehicular network instance with the objective of minimizing the number of agents, communication delay, communication cost, total payload, and total overhead. Due to the dynamic network topology, finding a new solution to the optimization is frequently required in order to avoid node isolation and redundant data transmission. Therefore, we propose a systematic way to collect data and make optimization decisions by inspecting the heterogeneous normalized network link entropy. The proposed optimization model for data collection for the hybrid SDVN architecture yields a 75.5% lower communication cost and 32.7% lower end-to-end latency in large vehicular networks compared to the data collection in the centralized SDVN architecture while collecting 99.9% of the data available in the vehicular network under optimized settings.
A Hydrogel-based Electronic Skin for Touch Detection Using Electrical Impedance Tomography
(MDPI AG, 2023-02-01) Zhang, Huiyang; Kalra, Anubha; Lowe, Andrew; Yu, Yang; Anand, Gautam
Recent advancement in wearable and robot-assisted healthcare technology gives rise to the demand for smart interfaces that allow more efficient human-machine interaction. In this paper, a hydrogel-based soft sensor for subtle touch detection is proposed. Adopting the working principle of a biomedical imaging technology known as electrical impedance tomography (EIT), the sensor produces images that display the electrical conductivity distribution of its sensitive region to enable touch detection. The sensor was made from a natural gelatin hydrogel whose electrical conductivity is considerably less than that of human skin. The low conductivity of the sensor enabled a touch-detection mechanism based on a novel short-circuiting approach, which resulted in the reconstructed images being predominantly affected by the electrical contact between the sensor and fingertips, rather than the conventionally used piezoresistive response of the sensing material. The experimental results indicated that the proposed sensor was promising for detecting subtle contacts without the necessity of exerting a noticeable force on the sensor.
Hybrid CNN-SVR Blood Pressure Estimation Model Using ECG and PPG Signals
(MDPI AG, 2023-01-22) Rastegar, Solmaz; Hosseini, Hamid Gholam; Lowe, Andrew
Continuous blood pressure (BP) measurement is vital in monitoring patients’ health with a high risk of cardiovascular disease. The complex and dynamic nature of the cardiovascular system can influence BP through many factors, such as cardiac output, blood vessel wall elasticity, circulated blood volume, peripheral resistance, respiration, and emotional behavior. Yet, traditional BP measurement methods in continuously estimating the BP are cumbersome and inefficient. This paper presents a novel hybrid model by integrating a convolutional neural network (CNN) as a trainable feature extractor and support vector regression (SVR) as a regression model. This model can automatically extract features from the electrocardiogram (ECG) and photoplethysmography (PPG) signals and continuously estimates the systolic blood pressure (SBP) and diastolic blood pressure (DBP). The CNN takes the correct topology of input data and establishes the relationship between ECG and PPG features and BP. A total of 120 patients with available ECG, PPG, SBP, and DBP data are selected from the MIMIC III database to evaluate the performance of the proposed model. This novel model achieves an overall Mean Absolute Error (MAE) of 1.23 2.45 mmHg (MAE ± STD) for SBP and 3.08 5.67 for DBP, all of which comply with the accuracy requirements of the AAMI SP10 standard.
Machine Learning Algorithm for NLOS Millimeter Wave in 5G V2X Communication
(AIRCC Publishing Corporation, 2020-12-13) Mohan, Deepika; Ali, GG Md Nawaz; Chong, Peter Han Joo
The 5G vehicle-to-everything (V2X) communication for autonomous and semi-autonomous driving utilizes the wireless technology for communication and the Millimeter Wave bands are widely implemented in this kind of vehicular network application. The main purpose of this paper is to broadcast the messages from the mmWave Base Station to vehicles at LOS (Line-ofsight) and NLOS (Non-LOS). Relay using Machine Learning (RML) algorithm is formulated to train the mmBS for identifying the blockages within its coverage area and broadcast the messages to the vehicles at NLOS using a LOS nodes as a relay. The transmission of information is faster with higher throughput and it covers a wider bandwidth which is reused, therefore when performing machine learning within the coverage area of mmBS most of the vehicles in NLOS can be benefited. A unique method of relay mechanism combined with machine learning is proposed to communicate with mobile nodes at NLOS.
The Need for Demonstrated Clinical Translational Evidence in Submissions to the IEEE Journal of Translational Engineering in Health and Medicine
(Institute of Electrical and Electronics Engineers (IEEE), 2026-05-15) Boyle, Gerard; Forner-Cordero, Arturo; Kalra, Anubha; Kremen, Vaclav; Ponsiglione, Alfonso Maria; Ranji, Mahsa; Sinha, Sharad; Tang, Xiangyang; Yang, Po; Reilly, Richard B
The IEEE Journal of Translational Engineering in Health and Medicine (JTEHM) exists at the intersection of biomedical engineering and clinical practice. Published articles go beyond laboratory proof-of-concept to provide tangible, real-world evidence of translation into clinical settings. This editorial provides the rationale for manuscripts submitted to IEEE JTEHM to demonstrate evidence of clinical translation. It also provides examples of acceptable forms of evidence and offers guidance to authors on how to meet this expectation. Clinical and Impact—By requiring demonstrated clinical translational evidence IEEE JTEHM endeavours to publish high-quality research with scientific novelty and practical clinical impact. This expectation strengthens the journal’s aim to accelerate the adoption of innovative solutions into healthcare systems and ultimately deliver quantifiable benefits to patients.
Perceptions Toward Using Artificial Intelligence and Technology for Asthma Attack Risk Prediction: Qualitative Exploration of Māori Views
(JMIR Publications, 2024-04-23) Widana Kankanamge, Darsha; Mirza, Farhaan; Bidois-Putt, Marie-Claire; Naeem, M Asif; Chan, Amy Hai Yan
BACKGROUND: Asthma is a significant global health issue, impacting over 500,000 individuals in New Zealand and disproportionately affecting Māori communities in New Zealand, who experience worse asthma symptoms and attacks. Digital technologies, including artificial intelligence (AI) and machine learning (ML) models, are increasingly popular for asthma risk prediction. However, these AI models may underrepresent minority ethnic groups and introduce bias, potentially exacerbating disparities. OBJECTIVE: This study aimed to explore the views and perceptions that Māori have toward using AI and ML technologies for asthma self-management, identify key considerations for developing asthma attack risk prediction models, and ensure Māori are represented in ML models without worsening existing health inequities. METHODS: Semistructured interviews were conducted with 20 Māori participants with asthma, 3 male and 17 female, aged 18-76 years. All the interviews were conducted one-on-one, except for 1 interview, which was conducted with 2 participants. Altogether, 10 web-based interviews were conducted, while the rest were kanohi ki te kanohi (face-to-face). A thematic analysis was conducted to identify the themes. Further, sentiment analysis was carried out to identify the sentiments using a pretrained Bidirectional Encoder Representations from Transformers model. RESULTS: We identified four key themes: (1) concerns about AI use, (2) interest in using technology to support asthma, (3) desired characteristics of AI-based systems, and (4) experience with asthma management and opportunities for technology to improve care. AI was relatively unfamiliar to many participants, and some of them expressed concerns about whether AI technology could be trusted, kanohi ki te kanohi interaction, and inadequate knowledge of AI and technology. These concerns are exacerbated by the Māori experience of colonization. Most of the participants were interested in using technology to support their asthma management, and we gained insights into user preferences regarding computer-based health care applications. Participants discussed their experiences, highlighting problems with health care quality and limited access to resources. They also mentioned the factors that trigger their asthma control level. CONCLUSIONS: The exploration revealed that there is a need for greater information about AI and technology for Māori communities and a need to address trust issues relating to the use of technology. Expectations in relation to computer-based applications for health purposes were expressed. The research outcomes will inform future investigations on AI and technology to enhance the health of people with asthma, in particular those designed for Indigenous populations in New Zealand.
SKS-Transformer: Multi-scale and Direction-aware Attention for Inertial Sensor-based Activity Recognition
(Frontiers Media S.A., 2026-01-23) Feng, Chengwei; Bačić, Boris; Li, Weihua; Xu, Hongqi
Introduction: Human Activity Recognition (HAR) has emerged as an enabling research field, with applications ranging from healthcare and sports analytics to smart environments. However, achieving scalable and accurate HAR systems that generalize across diverse activity scenarios remains a challenging problem. Methods: In this paper, we propose a scalable HAR system, which integrates a new model named SKS-Transformer with a custom-designed wearable Inertial Measurement Unit (IMU). The IMU combines an ESP8266 microcontroller and a JY61 sensor, enabling wireless acquisition of motion data. The proposed SKS-Transformer model incorporates Selective Kernel Networks and squeeze-enhanced axial attention modules to capture multiscale temporal dynamics and directional dependencies, respectively. The motion data preprocessing pipeline includes denoising, segmentation, and normalization. The preprocessed data are fused through a learnable gating mechanism, enabling the model to adaptively balance local and global motion patterns. Results: We evaluate the system scalability and performance on two public datasets (UCI-HAR and PAMAP2) and two captured datasets that feature both daily activities and fine-grained golf swing errors. Experimental results demonstrate that the SKS-Transformer model consistently surpasses the state of the art on both public datasets (by 0.3% and 0.09% compared to the best of 11 other published models) and by 2.86% and 0.46%, achieving the accuracy of up to 98.10% on collected HAR data, as well as 100% accuracy in golf swing error detection. Discussion: Ablation studies of SKS-Transformer confirm the contribution of each architectural model component to overall model performance and provide further insights for future optimizations. Future work will investigate the applications of the SKS-Transformer-based system in extended real-world scenarios, including intelligent healthcare, sports performance monitoring, and wearable computing. The source code for our proposed method has been released publicly and is available on GitHub at: URL: https://github.com/cw-feng/SKS-Transformer-Multi-scale-and-direction-aware-attention-for-activity-recognition
Predicting Aflatoxin M₁ in Raw Milk Using Machine Learning and Basic Measurements
(Elsevier, 2026-02-16) Ding, Haohan; Wang, Long; Song, Xiaodong; Cui, Xiaohui; Wilson, David I; Yu, Wei; Zhang, Cheng; Dong, Guanjun
Aflatoxin M₁ (AFM₁) is a carcinogenic and teratogenic mycotoxin that may be present in raw milk. Therefore, continuous monitoring of AFM₁ levels is essential to ensure dairy safety and regulatory compliance. Although laboratory-based analytical techniques such as ELISA and LC-MS/MS offer high accuracy, their cost, sample preparation requirements, and dependence on specialized personnel make them less practical for high-frequency or large-volume screening in dairy processing facilities. This creates a need for complementary, cost-effective prescreening approaches. This study proposed a qualitative AFM₁ prediction method based on routinely measured physicochemical indicators of raw milk, combined with machine learning algorithms. Five classical machine learning models were evaluated under a binary classification framework to determine whether AFM₁ levels exceed the regulatory threshold. Experimental results show that the multilayer perceptron achieves an accuracy and negative-sample recall rate above 80%, demonstrating the potential of machine learning as an effective prescreening tool for AFM₁. The findings provide a feasible direction for supporting rapid, economical, and large-scale monitoring of raw milk safety.
Critical Component Identification Study of Uncontrollable Heating Systems
(Springer, 2025-11-09) Mao, Ding; Han, Chong; Wang, Jay; He, Wei
As a complex thermal network ensuring civil and industrial energy supply, the failure of key components in heating systems can easily trigger cascading failures and large-scale heating outages. Aiming at the high computational complexity of traditional failure-simulation-based methods for key component identification, this study proposes a multi-dimensional index system and identification framework integrating graph-theoretic topology analysis and thermodynamic function evaluation. By constructing a modified betweenness index (topological importance), energy index and residual energy ratio (functional importance), and combining with system flow loss rate and user flow loss entropy (failure consequence indices), a complete quantitative evaluation system is formed. Verification using gridded heating system models with 4 to 25 nodes shows that the Spearman correlation coefficients between the modified betweenness and user flow loss entropy range from 0.82 to 0.93, and those between the energy index and system flow loss rate range from 0.65 to 0.96, with all coefficient of variation values less than 0.1, verifying the effectiveness and stability of the indices. The research results provide theoretical support for the efficient and accurate identification of key components in heating systems.
Aspect-adaptive Knowledge-based Opinion Summarization
(Springer Nature Singapore, 2024-11-15) Wang, Guan; Li, Weihua; Lai, Edmund; Bai, Quan
The increase in online information has overwhelmed users with opinions and comments on various products and services, making decision-making a daunting task. Text summarization can help by distilling long or multiple documents into concise, relevant content. Recent advances in Large Language Models (LLM) have shown great potential in this area. The existing text summarization approaches often lack the “adaptive” nature required to capture diverse aspects in opinion summarization, which is particularly detrimental to users with specific preferences. In this paper, we introduce an Aspect-adaptive Knowledge-based Opinion Summarization model for product reviews. This model generates summaries that highlight specific aspects of reviews, providing users with targeted, relevant information quickly. Our extensive experiments with real-world datasets explicitly demonstrate that our model surpasses current state-of-the-art methods. It effectively adapts to user needs, producing efficient, aspect-focused summaries that help users make informed decisions based on their unique preferences.
Semi-Analytical Pricing of Barrier Options in a Hybrid Model of Stochastic and Local Volatility
(MDPI AG, 2026-05-13) Cao, Jiling; Gong, Sheng; Li, Xi; Zhang, Wenjun
In this paper, the valuation of barrier options is studied when the underlying asset is driven by a hybrid model of stochastic volatility and constant elasticity of variance. Using an asymptotic expansion approach and the Fourier transform method, a semi-analytical approximate pricing formula for up-and-out call options are derived under the proposed hybrid model. We validate the approximate pricing formula by comparing its outputs with those produced by Monte Carlo simulation and the binomial tree method. In addition, we perform a sensitivity analysis numerically on the key model parameters and investigate limiting regimes of the hybrid model. It is verified that the approximation is properly anchored to simpler benchmark models when one or both perturbative effects vanish.
A Review of the Structure of Free-Space Optical Channel Models: Physical Meaning, Assumptions, and Atmospheric Conditions
(MDPI AG, 2026-04-26) Phuchortham, Sabai; Sabit, Hakilo
Free-space optical (FSO) communication is an attractive high-capacity wireless technology for terrestrial, aerial, and satellite links. However, FSO performance is strongly affected by multiple impairments, including path loss, turbulence attenuation, pointing errors, and equipment loss. Therefore, accurate performance evaluation requires channel modelling that accounts for both deterministic power losses and stochastic channel effects. This paper presents a comprehensive and structured review of FSO channel modelling, covering the transmission, propagation medium, and receiver sections. The composite channel response is represented using a mathematical formulation. Commonly used FSO models are reviewed and organised, including Beer–Lambert and geometrical loss, Kim and Kruse path loss models, Lognormal, Gamma–Gamma, K, and Málaga distributions, along with pointing-error and angle-of-arrival models. Each model is explained in terms of its physical meaning, assumptions, and applicable operating conditions. Lastly, a numerical example is presented to demonstrate how deterministic losses and stochastic channel effects can be combined in FSO performance evaluation.
Quantitative Analysis of Information Security and Privacy Challenges in Government Cloud Service Adoption
(MDPI AG, 2026-05-02) Ukeje, Ndukwe; Gutierrez, Jairo A; Petrova, Krassie
The government’s adoption of cloud computing is critical for digital transformation, but it faces persistent concerns over information security, privacy, governance, and risk. This study examines the factors influencing a government’s intention to adopt cloud services, adapting the Unified Theory of Acceptance and Use of Technology (UTAUT) with constructs tailored to the public sector. A cross-sectional survey was conducted across 90 Nigerian government organisations, producing 230 valid responses from IT professionals, administrators, and policy personnel. The statistical analysis of the data was conducted using SPSS and structural equation modelling in AMOS. Validity and reliability were confirmed through composite reliability, Cronbach’s alpha, and discriminant validity measures. Findings show that privacy (β = 0.11, p < 0.05), governance framework (β = 0.34, p < 0.001), performance expectancy (β = 0.38, p < 0.001), and information security (β = 0.10, p < 0.05) significantly influence government intention to adopt cloud services. Performance expectancy emerged as the strongest predictor. Contrary to expectations, perceived risk did not significantly moderate the relationships, and interaction terms were non-significant. The final model explained 45% of the variance in adoption intention (R2 = 0.45). The study highlights the importance of strengthening governance frameworks, emphasising tangible performance outcomes, and positioning information security and privacy as an enabler of adoption rather than a barrier. By adapting UTAUT to the government context and disentangling the role of perceived risk, the study offers both theoretical refinement and practical guidance for policymakers aiming to accelerate digital transformation and secure cloud adoption.
Deep Belief Network-Based Activity of Daily Living Monitoring for Fall Risk Prediction in Elderly
(Institute of Electrical and Electronics Engineers (IEEE), 2026-04-28) Mohan, Deepika; Chong, Peter Han Joo; Gutierrez, Jairo; Baig, Mirza Mansoor; Li, Hui
Despite advancements in healthcare and emerging technologies, falls among older adults remain a significant health issue. Recent research has increasingly focused on developing advanced monitoring methods, predicting, and preventing falls in this population. Achieving high performance in fall prediction requires a clear understanding of relevant features such as gait patterns, balance metrics, muscle strength, and environmental factors. Identifying these key indicators and incorporating data from wearable sensors, medical histories, and demographic information can significantly enhance predictive accuracy. This study proposes an intelligent fall prediction model that anticipates future falls in older adults by continuously monitoring their Activities of Daily Living (ADLs) and detecting abnormalities. The model uses a Deep Belief Network (DBN) that incorporates contrastive divergence for pre-training, backpropagation for fine-tuning, and the Adam Optimizer to minimize loss. Evaluation of the proposed model shows it achieved an accuracy of 91.67%, specificity of 100%, and sensitivity of 90.00% when compared to the Ground Truth (GT) and existing fall prediction approaches. These results suggest that advanced deep learning techniques can effectively assist in early fall risk prediction, potentially reducing the likelihood and severity of falls among older adults.

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