Digital Commons Network^™

A Transdisciplinary Approach To Advancing Water Security And Public Health: The “Nurse + Engineer” Model For Achieving Sustainability In Wash Systems Using Community-Based Participatory Research, Daniel B. Oerther, Sarah Oerther

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

Human health is linked to the health of the environment, a concept referred to as planetary health. As environmental challenges become increasingly complex, single-disciplinary approaches are insufficient to achieve sustainable solutions. This article presents a transdisciplinary framework, termed the "Nurse + Engineer" model, as a novel methodology for addressing complex problems at the nexus of environmental engineering and public health. The model integrates the technical design and systems-level thinking of environmental engineering with the community-based, patient-centered care of nursing. This work details the key lessons learned from the retrospective application of this framework to a series of multi-year, community-based participatory …

2024 Annual Operations And Maintenance Report 2024 Bres Annual Summary Report For Work Completed In Quadrant 4, Butte-Silver Bow Department Of Reclamation And Environmental Services

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Artificial Intelligence Adoption Among Future Journalism And Mass Communications Professionals: A Tam-Based Approach To Students’ Generative Ai Adoption Intent, Chinwendu P. Akalonu

Journal of Communication Pedagogy

As artificial intelligence (AI) reshapes industries, journalism and mass communications professions are slowly adapting to these technologies. This study employs the Technology Acceptance Model (TAM) and AI-TAM frameworks to examine predictors of AI adoption intent among college students preparing for careers in the field. Survey results reveal that perceived usefulness, trust in AI output quality, organizational requirement, ethical awareness, and certain sociodemographic factors such as race predict AI adoption intent. Additionally, the study highlights differences in adoption intent among students in different subfields of mass communication, including journalism, advertising, and public relations. It underscores the importance of expanding traditional TAM-based …

Development And Application Of A Frailty Index Derived From Patient-Reported Outcome Measures And Comorbidities In Oncology Patients, Divya Mathew, Heather Lane, David Ward, Rosemary Saunders, Lesley Millar, Christopher Etherton-Beer

Research outputs 2022 to 2026

Background: Emerging evidence supports the use of a frailty index as an objective measure of frailty. We constructed a frailty index from patient-derived outcome measures, as well as self-reported comorbidity data, and tested its correlation with survival.

Method: Data from 464 patients with lung, breast and colorectal cancer were obtained from the Continuous Improvement in Care – Cancer Project database, and a 42-variable frailty index was generated by dividing the total number of deficits present by the total number assessed and expressed as a ratio between 0 and 1. Frailty was defined as a frailty index of >0.2. Its association …

Crafting The Inspirational Teacher: Governing Through Affect As Policy Response To Teacher Recruitment In Australia, Saul Karnovsky, Brad Gobby, George Variyan, Andrew W. Wilkins, Antonio Olmedo

Research outputs 2022 to 2026

As policymakers address a teacher attrition crisis, this paper examines how the Australian policy response mobilises affect to address and manage teacher recruitment. Drawing on affect theory and governmentality, we analyse a case study–the Australian Government’s Be That Teacher campaign–as a policy instrument that seeks to cultivate among would-be-teachers desirable feelings and aspirations aligned with state priorities. Our analysis shows how a sentimental and idealised figure of the ‘inspirational’ teacher is mobilised as a form of affective governmentality through two interconnected dynamics: (1) the construction of teaching as emotionally fulfilling and morally meaningful work through narratives of inspiration, care, and …

Crisis, Credit Risk, And Productivity: Lessons From The Vietnamese Banking Sector During Covid-19, Van Nguyen, Phuong Thanh Le, Reza Kiani Mavi, Amir Arjomandi

Research outputs 2022 to 2026

This study investigates how an unprecedented systemic shock, the COVID-19 pandemic, reshaped bank productivity and intermediation efficiency in Vietnam, one of the fastest-growing emerging economies. We develop a novel risk-adjusted total factor productivity (TFP) measure based on the by-production Luenberger–Hicks–Moorsteen (LHM) indicator, which incorporates non-performing loans as undesirable outputs to better capture credit-risk dynamics. Using a balanced panel covering both the pre-pandemic and pandemic periods (2016–2022), our results show that Vietnamese banks experienced a general decline in productivity before and during COVID-19, driven largely by reductions in technical and scale efficiency. However, foreign and state-owned banks recorded stronger technological progress …

Effects Of Immediate Skin-To-Skin Contact In Preterm Infants In High-Resource Settings: A Systematic Review, Victoria Rettedal, Siren Rettedal, Maren Johanne Heilskov Rytter, Linea Grothe Larsen, Frederikke Buskov, Evalotte Mörelius, Katrin Mehler, Karoline Lode-Kolz

Research outputs 2022 to 2026

Aim: A landmark trial published in 2021 demonstrated a 25% reduction in neonatal mortality when comparing immediate skin-to-skin contact (iSSC) to conventional care in infants with birthweight 1–1.8 kg in low- and middle-resource settings. New evidence from randomised clinical trials on iSSC in high-resource settings has recently become available. This systematic review aimed to evaluate evidence for immediate/early SSC, defined as initiated within 1 h after birth, versus conventional care in inborn preterm infants in high-resource settings.

Methods: Ten electronic databases, including PubMed, Embase, and Cochrane CENTRAL, from 2000 until February 2026, were searched. Randomised clinical trials comparing iSSC to …

Employer Perspectives On Graduates’ Professional Identity Formation In The Workplace, Denise Jackson, Michael Tomlinson

Research outputs 2022 to 2026

Professional identity (PI) is recognised as a crucial dimension in how graduates negotiate the transition to employment and move towards a pathway of sustainable professional development. Perspectives on PI have been derived mainly from higher education students with little attention given to the perspective of employers involved in supporting and supervising graduates in the workplace. Accordingly, the study surveys 339 industry professionals to examine their perceptions of how formed graduates’ PI is at different times after recruitment and the influence of personal, organisational and external factors on identity formation at work. Employers recognised how a well-formed PI enables graduates to …

Fostering Authentic Corporate Social Responsibility Through Place-Based And Relational Methodologies In Mining Governance: A Global Synthesis, Mehran Nejati, Eduardo Ordonez-Ponce, Rumen Rachev, Belen Lopez Vazquez

Research outputs 2022 to 2026

Purpose: As the global economy shifts towards low-carbon development, sustainable mining practices are becoming increasingly vital for balancing resource extraction with environmental and social responsibilities. This study aims to critically evaluate how traditional corporate social responsibility (CSR) frameworks fall short in addressing power asymmetries and territorial impacts, and to explore how place-based and relational methodologies can foster authentic corporate social responsibility (ACSR) in mining governance.

Design/methodology/approach: This review synthesises findings from 77 peer-reviewed studies published between 2015 and 2025, covering diverse global mining contexts. The analysis identifies transformative themes through a qualitative meta-synthesis approach, focusing on governance practices that empower …

It Will Only Work If Everyone Is On Board: A Needs And Context Assessment To Inform Exercise Oncology Implementation Within A Public Healthcare Organisation, Georgia L. White, Mary A. Kennedy, Bryan A. Chan, Hattie Wright, Grace L. Rose

Research outputs 2022 to 2026

Introduction: Exercise has well-established benefits across the cancer continuum for people living with and beyond cancer, yet the exercise oncology field faces a research-to-practice gap. Understanding the factors that influence implementation in any given context is essential to ensure implementation strategies have the greatest likelihood of success. The study mapped onsite exercise oncology services, staff-reported implementation barriers and facilitators, organisational readiness, and experiences reported by people living with or beyond cancer, across a large public healthcare organisation.

Methods: Staff and people living with and beyond cancer across cancer care centres within the public healthcare organisation completed an online survey. Data …

It's The Most Diverse Job I’Ve Ever Had: What Do Registered Nurses Working In Australian Schools Do?, Anita Moyes

Research outputs 2022 to 2026

Although nurses have been working in Australian schools for many decades, research has focused primarily on narrow aspects of clinical practice or service delivery in specific regions. The aim of this study was to identify the domains of practice registered nurses report in a typical working day in Australian schools, using interview data from a national sample. Forty-five nurses working in government and nongovernment schools in every state and territory of Australia participated. Interviews were analyzed using inductive content analysis ( Elo & Kyngas, 2008). Six distinct domains of practice characterized nursing work in Australian schools: (i) responding to diverse …

Making Room For ‘Feelings’: What's At Stake For The Theory Of Practice Architectures?, George Variyan, Brad Gobby

Research outputs 2022 to 2026

This paper responds to Thelin, K. [2024. What about feelings? Elaboration on and consideration of the theoretical construct of emotions in practice and practice architectures. International Journal of Leadership in Education, 1–14] intriguing proposal that the theory of practice architectures (hereafter TPA) would benefit from an augmentation to its oft-repeated analytical mantra of ‘sayings, doings and relatings’ by adding the notion of feelings. Thelin's work is motivated by the increasing realisation amongst adherents that TPA has thus far been relatively silent on how emotions mediate practice. As such, there are ongoing efforts to think through the place of emotions in …

Multi-Scale Sensor-Aware Variational Autoencoders For Adaptive Iot Security: Integrating Drift Calibration And Cyberattack Detection, Md Kamal Hossain, Iftekhar Ahmad, Daryoush Habibi

Research outputs 2022 to 2026

Existing approaches to Industrial Internet of Things (IoT) anomaly detection treat sensor drift calibration and cyberattack detection as separate problems, overlooking their strong interdependence and the compound failure modes that arise when drift dynamics are exploited by stealthy adversaries. Industrial Internet of IoT sensor networks therefore require a unified framework that jointly models both phenomena. This paper proposes the first unified multi-scale sensor-aware variational autoencoder (MS-VAE) framework that jointly models sensor drift and malicious activity within shared latent representations, enabling integrated calibration and security monitoring. The framework introduces three key components: (i) a multi-scale latent architecture that captures short-term anomalies …

Nurses' Perceptions Of Their Non-Technical Skills In Recognising And Responding To Clinical Deterioration In Hospital Settings: A Systematic Review, Darlene Archer, Amineh Rashidi, Rajbeer Kaur, Allison Roderick, Olivia Gallagher

Research outputs 2022 to 2026

Aim: To understand nurses' perceptions of non-technical skills in recognising and responding to clinical deterioration in hospital settings.

Design: A mixed methods systematic review was undertaken, guided by the Joanna Briggs Institute Manual for Evidence Synthesis.

Methods: A systematic search of the primary research literature published between 2012 and October 2025 was undertaken. A convergent integrated approach to synthesis, without meta-analysis, was undertaken.

Data Sources: The MEDLINE, CINAHL, Scopus, PsychInfo and Embase electronic databases were searched. Reference lists of full-text articles retrieved were reviewed for additional data sources.

Results: Twenty-four primary studies were included. Narrative synthesis resulted in six main …

Selective Human Resource Practices, Psychological Safety Climate And Employee Wellbeing, Yvonne Brunetto, Matthew J. Xerri, Geremy Farr-Wharton

Research outputs 2022 to 2026

Purpose: Engineers and healthcare professionals perceive higher levels of stress compared with the general population, suggesting that past human resource management (HRM) systems have failed to manage them appropriately. This study examines whether psychosocial safety climate (PSC) acts as a psychological conduit in translating HRM practices and mediating their impact on wellbeing using the common good HRM (CGHRM) and Conservation of Resources theoretical frameworks.

Design/methodology/approach: Data were collected using surveys collected at two points – one-month apart, and then analysed using structural equation modelling (Analysis of Moment Structures) to manipulate survey data from 300 matched employees.

Findings – The variance …

When The Panic Sets In: Ai-Proofing As Pedagogical Violence In Social Work Education, Norah Hosken, Susan Bailey

Research outputs 2022 to 2026

Universities’ rushed responses to generative Artificial Intelligence (AI) constitute pedagogical violence against students and educators in social work programs. Drawing on Matusov and Sullivan’s pedagogical violence framework and Bourdieu’s symbolic violence, we examine how institutional mandates to ‘AI-proof’ assessments contradict core social work values while inflicting systematic harm. AI-proofing policies impose time-limited assessments disadvantaging disabled students and those with caring responsibilities, deploy surveillance technologies that discriminate against non-native English speakers, demand unsustainable workload increases from burned-out educators, and dismantle scaffolded pedagogy aligned with how social workers learn to practise. Using collaborative autoethnography, we document our experiences across Australian universities and …

Multi-Parameter Optimization And Adaptive Temperature Compensation For Fbg Strain Sensors In Wide-Temperature-Range Aerospace Applications, Ruling Zhou, Jiacheng Yu, Yutang Dai, Jianguan Tang, Minghong Yang, Farhan Mumtaz

Electrical and Computer Engineering Faculty Research & Creative Works

Accurate strain measurement in cryogenic fuel pipelines is crucial for ensuring the structural integrity and reliability of rocket engine systems operating under extreme thermal conditions. Fiber Bragg grating (FBG) sensors show significant potential for such applications; however, their inherent temperature-strain cross-sensitivity limits performance over wide temperature ranges. This research presents an enhanced compensation strategy combining multi-parameter optimization with temperature-zone-specific adaptation to improve the accuracy and stability of FBG-based strain sensing in harsh aerospace environments. Four special steel substrate materials including S03, S06, S07, and 1Cr18Ni9Ti, were evaluated using strain transfer theory and thermo-mechanical coupling simulations. Genetic algorithms optimized key design …

Safe Optimal Control Framework For Cooperative Manipulation Of Objects In Human–Robot Teams, Irfan Ganie, Sarangapani Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This article introduces a distributed deep neural network (NN)-based adaptive control framework for cooperative object manipulation in human–robot teams with unknown agent dynamics by using three distinct multilayer NN observers (MNNOs). The first observer, termed the reference point estimator, enables each robotic agent to estimate the object's reference center using consensus-based learning, even without direct access to global reference trajectories. The second observer, referred to as the human force-to-trajectory estimator, uses human-applied forces to infer the intended position, velocity, and acceleration of the object, enabling real-time estimation of human intent. Together, these two observers allow distributed estimation of human-intended motion. …

Waveguide-Assisted Single-Mode Fiber Bragg Gratings In A Highly Multimode Coreless Fiber Via Femtosecond Laser Inscription For Extreme Temperature Quasi-Distributed Thermal Sensing, Farhan Mumtaz, Koustav Dey, Bohong Zhang, Jeffrey D. Smith, Ronald J. O'Malley, Jie Huang

Electrical and Computer Engineering Faculty Research & Creative Works

This research reports a potential quasi-distributed thermal mapping optical sensing system for extreme temperatures, leveraging femtosecond (fs) laser inscribed single-mode fiber Bragg gratings (FBGs) and a waveguide within coreless, highly multimode optical fiber, resulting in a single-mode structure. Unlike doped single-mode fibers, coreless fibers composed of silica rods prevent issues associated with dopant migration and ensure data accuracy. The strategic placement of point-by-point FBGs in a cascaded formation on the fs-laser inscribed waveguide facilitates localized multipoint sensing. The long-term stability of the proposed waveguide-assisted FBG system was assessed over 24 hours at elevated temperatures (1000°C), showing no hysteresis during heating …

Self-Calibrating Uav Navigation: Reinforcement Learning Approaches For Horizontal Trajectory Estimation, Shirin Nasr-Esfahani, S. Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

Accurate unmanned aerial vehicle (UAV) trajectory estimation is essential for autonomous navigation, particularly in GPS-denied environments. Visualodometry and simultaneous localization and mapping (SLAM) approaches require precise camera intrinsic parameters, which are typically obtained through predefined or offline calibration. Instead, in this work, we propose a reinforcement learning (RL)-based self-calibration framework that estimates camera intrinsic parameters directly from monocular video sequences, without requiring prior knowledge of the camera, environment, or calibration targets. This intrinsic parameter estimation is then leveraged to achieve robust UAV trajectory estimation using only video data. We formulate the problem as a sequential decision-making task, where an RL …

Performance Analysis And Optimization Of Fructose Memristor-Based Neuromorphic Systems, Harshvardhan Uppaluru, Shakil Mahmud Jiban, Shah Zayed Riam, Feng Zhao, Jinhui Wang

Electrical and Computer Engineering Faculty Research & Creative Works

Natural organic memristors have demonstrated promising synaptic behavior, positioning them as strong candidates for synaptic devices in neuromorphic systems. This paper presents the fabrication and evaluation of a neuromorphic system based on natural organic 16-level and 32-level fructose memristors. First, the manufacturing process of fructose memristors is described in detail. Second, the nonlinear property associated with fructose memristors is investigated, and an optimization method is applied to address the nonlinear effects. The performance of the fructose memristor-based neuromorphic system on MNIST with a multi-layer perceptron and on CIFAR-10 with VGG-8 is evaluated and reported under various conditions -with/without nonlinearity optimization …

Rf-Attennet: A Hybrid Attention-Enhanced Network For Mixed Signal Classification In Uav Swarm Detection, Prajoy Podder, Mohammad Atikur Rahman, Maciej Zawodniok, Sanjay Madria

Electrical and Computer Engineering Faculty Research & Creative Works

The continuous increase of UAVs, particularly in swarms, creates significant challenges for security and airspace regulation. Traditional RF fingerprinting methods struggle to detect and classify UAV swarms due to overlapping signals and interference. This study introduces RF-AttenNet, a hybrid deep learning model designed to classify mixed UAV signals by analyzing composite RF spectrograms. RF-AttenNet uses dual attention mechanisms, channel and spatial attention to focus on critical spectral features, enabling the model to effectively separate and identify overlapping UAV signals. We have developed custom composite UAV datasets that simulate real-world swarm interference, incorporating both single and mixed UAV classes. RF-AttenNet achieves …

Fixed-Time Consensus Tracking For Nonlinear Multi-Agent Systems Under Aperiodically Intermittent Control, Lei Xue, Tong Wu, Wenwen Jia, Donald C. Wunsch

Electrical and Computer Engineering Faculty Research & Creative Works

This article explores the problem of fixed-time consensus tracking (FT-CT) for nonlinear multi-agent systems utilizing the a periodically intermittent control (AIC) strategy. In contrast to existing control algorithms, the proposed algorithm utilizes the AIC strategy instead of the conventional continuous-time control strategy, effectively reducing the consumption of communication resources. Moreover, the problem of intermittent FT-CT is well handled by proposing the average control rate of the AIC strategy. Two theorems based on the cases of directed and undirected graphs are proposed, respectively. Finally, the validity of these results is confirmed through numerical simulations on a general nonlinear system and a …

Resolving Stiffness Trade-Offs In Simultaneous Pressure And Vibration Sensing Using A Corrugated-Tube Fiber-Optic Sensor, Yizheng Chen, Yan Tang, Jie Huang, Qi Zhang, Biyao Shi, Zewei Wu

Electrical and Computer Engineering Faculty Research & Creative Works

This article proposes and experimentally demonstrates a corrugated-tube-based fiber-optic sensor capable of measuring pressure, vibration, or both simultaneously. To address the limited sensitivity of conventional diaphragm-based designs, the sensor incorporates an optimized corrugated tube that balances the conflicting stiffness requirements for pressure and vibration measurements. The corrugated tube, acting as a mechanical transducer, is integrated with an extrinsic fiber-optic Fabry–Perot interferometer (EFPI). The EFPI cavity is formed between a reflective surface at the sealed end of the corrugated tube and the cleaved end face of an optical fiber fixed within a mounting assembly. In this configuration, displacement of the corrugated …

Nash Equilibrium Strategies For Multicluster Pursuit–Evasion Game With Disturbances: A Prescribed-Time Convergence Approach, Lei Xue, Xian Yu, Yongbao Wu, Jian Liu, Changyin Sun, D. C. Wunsch

Electrical and Computer Engineering Faculty Research & Creative Works

This article investigates the problem of prescribed-time Nash equilibrium (NE) seeking for a multicluster pursuit–evasion game (PEG) subject to external disturbances. To mitigate the impact of disturbances and reach the NE within a user-defined prescribed time, a prescribed-time disturbance observer (PTDO) is devised to estimate and compensate for them. Based on this observation, a novel control algorithm is developed, which facilitates collaboration among multiple pursuers to capture multiple evaders within the prescribed time. It is theoretically demonstrated that the designed algorithm ensures prescribed-time convergence to the NE of the multicluster PEG with disturbances. Finally, numerical simulations are conducted to verify …

Real-Time Multiregional Market-To-Market Congestion Management Through Exchange Of Relief Cost Curve, Haotian Chen, Yonghong Chen, Jose Daniel Lara, Jarrad Wright, Matthew Bossart, Sebastian De Jesus Machado, Rui Bo

Electrical and Computer Engineering Faculty Research & Creative Works

This paper introduces a novel method for multiregional market-to-market (M2M) coordinated congestion management. It identifies shortcomings in existing M2M approaches, where Regional Transmission Organizations (RTOs) exchange shadow prices and relief requests to optimize congestion relief allocations across interconnected regions. Two methods are proposed to enhance flow and price convergence. The first method proposes that both Regional Transmission Organizations (RTOs) use state-estimator flows directly to determine relief requirements, eliminating delays and potential oscillations caused by using market flows calculated from the prior period under existing M2M approach. The second method involves exchanging transmission relief cost curves, enabling each RTOs to integrate …

Transformer-Customer Relationship Identification Based On Load-Switching Fluctuation Characteristics Considering Same-Feeder-Adjacent-Transformer Condition, Yanan Zhang, Gan Zhou, Yuyuan Liu, Wei Gu, Yanjun Feng, Yujue Wang, Rui Bo

Electrical and Computer Engineering Faculty Research & Creative Works

Accurately identifying the connectivity between transformers and downstream three-phase customers in low-voltage distribution networks is challenging, because voltage curves of different phases and nearby nodes can be weakly distinguishable, especially when adjacent transformers on the same feeder serve geographically close customers with highly similar voltage curves. This paper proposes a novel method based on load-switching fluctuation characteristics recorded by smart meters. By extracting localized current and voltage fluctuations and establishing correlation matching, the method overcomes the limited discriminability using steady-state measurements. The method operates in two stages: first, switching-induced fluctuation characteristics are extracted and matched to cluster customers by the …

Lidar-Based Framework For Detecting Suspicious Human Activities, Ahd Aljumah, Charalampos Antoniadis, Hakim Ghazzai, Nawfal Guefrachi, Ahmad Alsharoa, Gianluca Setti

Electrical and Computer Engineering Faculty Research & Creative Works

This study explores the development of Human Activity Recognition (HAR) systems capable of identifying suspicious activities to enhance security in public spaces. We propose an innovative solution that integrates LiDAR sensors with deep learning technologies. Our method employs advanced models operating on LiDAR point cloud, PV-RCNN for human detection, and LidarGait++ for classifying activities into categories such as standing or walking (non-suspicious) and sneaking or fighting (suspicious). Due to the scarcity of suitable real-world datasets for training such systems, we utilize a 3D simulation tool, Blender, to create realistic environments and generate labeled point cloud data. This synthetic dataset allows …

Attitudes And Expectations Towards Acupuncture For Primary Dysmenorrhoea: Findings From A Cross-Sectional Survey In Australia, Weiting Liu, Amanda Towell-Barnard, Khui Hung Lee, Timothy Leen Kang

Research outputs 2022 to 2026

Objective: To describe attitudes towards acupuncture and expectations regarding potential treatment outcomes among women with self-reported primary dysmenorrhoea (PD) residing in Australia. Methods: A cross-sectional, descriptive online survey was conducted to assess attitudes and treatment expectancy using an adapted version of previously published complementary and alternative medicine (CAM) attitude scale and a validated acupuncture expectancy scale (AES). Data were analysed descriptively, with exploratory analysis conducted to support the interpretation of response patterns. Ethical approval was obtained from the Edith Cowan University Human Research Ethics Committee (2023-04471-LIU) and participants provided electronic informed consent. Results: A total of 247 responses were received. …

Challenging The Monopoly Giant: Market Challenger Premium As Competitive Advantages, Cheuk Hang Au, Chih Yuan Chou

Research outputs 2022 to 2026

Market challengers frequently disrupt established incumbents with innovative ideas or novel business models. Yet, they frequently face high entry barriers and the entrenched power of incumbents. Even when challengers provide products or services better aligned with the market, competing effectively can remain difficult, particularly in monopoly-dominated sectors. This paper presents a case study of ViuTV entering Hong Kong’s monopoly-led free-to-air TV market. We identify IFH (innovation, freshness, hope) strategies for challengers to gain competitive advantage, leading to the emergence of a “market challenger premium” as a unique competitive edge. We also examine the role of customer cognitive biases and the …