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- ItemA Plasmid System That Utilises Phosphoribosylanthranilate Isomerase to Select Against Cells Expressing Truncated Proteins(MDPI AG, 2025-03-14) Ghuge A; Gottfried S; Schiemann A; Sattlegger E
- ItemRelationship between the three dimensions of paternalistic leadership, cognitive and affective trust and organizational citizenship behavior: a multilevel mediational pathway(Emerald Publishing Limited, 2025-02-25) Lee MCCPurpose The current study aims to explore the three dimensions of paternalistic leadership (i.e. moral leadership, benevolent leadership and authoritarian leadership) and their dual pathways of positive and negative influences on employees’ organizational citizenship behavior through the two aspects of trust (i.e. cognitive and affective trust). Design/methodology/approach Given that trust is pertinent in any human relationship, especially in Asian countries where bonding plays an important role, the current study investigated the relationship of each leadership style within paternalistic leadership on employees’ cognitive and affective trust in their leaders, employees’ organizational citizenship behavior and the processes involved. The current study employed a cross-sectional multilevel approach with 435 employees from 85 workgroups participating in the study. Findings As hypothesized, benevolent and moral leadership styles (but not the authoritarian leadership style) had a positive effect on employees’ cognitive and affective trust in their leaders and on employees’ organizational citizenship behavior. Cognitive and affective trust also mediated the relationships of benevolent and moral leadership styles with organizational citizenship behavior. Originality/value The study’s findings urge practitioners and human resources personnel to be aware of the dual effects that a paternalistic leader has on employees. To be specific, benevolent and moral leadership styles are conducive to employees’ work outcomes, whereas the authoritarian leadership style has a non-significant role in employees’ work outcomes.
- ItemMultimodal Deep Learning for Android Malware Classification(MDPI (Basel, Switzerland), 2025-02-28) Arrowsmith J; Susnjak T; Jang-Jaccard J; Buccafurri FThis study investigates the integration of diverse data modalities within deep learning ensembles for Android malware classification. Android applications can be represented as binary images and function call graphs, each offering complementary perspectives on the executable. We synthesise these modalities by combining predictions from convolutional and graph neural networks with a multilayer perceptron. Empirical results demonstrate that multimodal models outperform their unimodal counterparts while remaining highly efficient. For instance, integrating a plain CNN with 83.1% accuracy and a GCN with 80.6% accuracy boosts overall accuracy to 88.3%. DenseNet-GIN achieves 90.6% accuracy, with no further improvement obtained by expanding this ensemble to four models. Based on our findings, we advocate for the flexible development of modalities to capture distinct aspects of applications and for the design of algorithms that effectively integrate this information.
- ItemAccelerating Disease Model Parameter Extraction: An LLM-Based Ranking Approach to Select Initial Studies for Literature Review Automation(MDPI (Basel, Switzerland), 2025-03-26) Sujau M; Wada M; Vallée E; Hillis N; Sušnjak T; Verspoor KAs climate change transforms our environment and human intrusion into natural ecosystems escalates, there is a growing demand for disease spread models to forecast and plan for the next zoonotic disease outbreak. Accurate parametrization of these models requires data from diverse sources, including the scientific literature. Despite the abundance of scientific publications, the manual extraction of these data via systematic literature reviews remains a significant bottleneck, requiring extensive time and resources, and is susceptible to human error. This study examines the application of a large language model (LLM) as an assessor for screening prioritisation in climate-sensitive zoonotic disease research. By framing the selection criteria of articles as a question–answer task and utilising zero-shot chain-of-thought prompting, the proposed method achieves a saving of at least 70% work effort compared to manual screening at a recall level of 95% (NWSS 95%). This was validated across four datasets containing four distinct zoonotic diseases and a critical climate variable (rainfall). The approach additionally produces explainable AI rationales for each ranked article. The effectiveness of the approach across multiple diseases demonstrates the potential for broad application in systematic literature reviews. The substantial reduction in screening effort, along with the provision of explainable AI rationales, marks an important step toward automated parameter extraction from the scientific literature.
- ItemNutrient-Level Evaluation of Meals Provided on the Government-Funded School Lunch Program in New Zealand(MDPI (Basel, Switzerland), 2022-12) de Seymour J; Stollenwerk Cavallaro A; Wharemate-Keung L; Ching S; Jackson J; Maeda-Yamamoto MApproximately 1 in 6 children in New Zealand are living in households facing poverty and 14% of the population is food insecure. The Ka Ora, Ka Ako|Healthy School Lunches program aims to reduce food insecurity by providing access to a nutritious lunch every school day. This study analyzed the nutritional content of Ka Ora, Ka Ako meals and compared them to national and international standards. Meals were selected at random from approved menus. The suppliers covered by the 302 meals analyzed provide 161,699 students with a lunch (74.9% of students on the program). The meals were analyzed using Foodworks 10 nutrient analysis software. The nutrient content was compared against the New Zealand/Australia Nutrient Reference Values (NRVs) and to nutrient-level standards for international school lunch programs. A total of 77.5% of nutrients analyzed exceeded 30% of the recommended daily intakes. Protein, vitamin A and folate met the NRV targets and a majority of the international standards (55/57). Energy, calcium, and iron were low compared to NRVs and international standards (meeting 2/76 standards). Carbohydrates were low compared to international standards. The findings have been used to inform the development of revised nutrition standards for the program, which will be released in 2022.
- ItemA review of climate change impact assessment and methodologies for urban sewer networks(Elsevier B V, 2025-06) Karimi AM; Jelodar MB; Susnjak T; Sutrisna MUnderstanding how climate change affects urban sewer networks is essential for the sustainable management of these infrastructures. This research uses a systematic literature review (PRISMA) to critically review methodologies to assess the effects of climate change on these systems. A scientometric analysis traced the evolution of research patterns, while content analysis identified three primary research clusters: Climate Modelling, Flow Modelling, and Risk and Vulnerability Assessment. These clusters, although rooted in distinct disciplines, form an interconnected framework, where outputs of climate models inform flow models, and overflow data from flow models contribute to risk assessments, which are gaining increasing attention in recent studies. To enhance risk assessments, methods like Gumbel Copula, Monte Carlo simulations, and fuzzy logic help quantify uncertainties. By integrating these uncertainties with a Bayesian Network, which can incorporate expert opinion, failure probabilities are modelled based on variable interactions, improving prediction. The study also emphasises the importance of factors, such as urbanisation, asset deterioration, and adaptation programs in order to improve predictive accuracy. Additionally, the findings reveal the need to consider cascading effects from landslides and climate hazards in future risk assessments. This research provides a reference for methodology selection, promoting innovative and sustainable urban sewer management.
- ItemTransfer learning on transformers for building energy consumption forecasting—A comparative study(Elsevier B V, 2025-06-01) Spencer R; Ranathunga S; Boulic M; van Heerden AH; Susnjak TEnergy consumption in buildings is steadily increasing, leading to higher carbon emissions. Predicting energy consumption is a key factor in addressing climate change. There has been a significant shift from traditional statistical models to advanced deep learning (DL) techniques for predicting energy use in buildings. However, data scarcity in newly constructed or poorly instrumented buildings limits the effectiveness of standard DL approaches. In this study, we investigate the application of six data-centric Transfer Learning (TL) strategies on three Transformer architectures—vanilla Transformer, Informer, and PatchTST—to enhance building energy consumption forecasting. Transformers, a relatively new DL framework, have demonstrated significant promise in various domains; yet, prior TL research has often focused on either a single data-centric strategy or older models such as Recurrent Neural Networks. Using 16 diverse datasets from the Building Data Genome Project 2, we conduct an extensive empirical analysis under varying feature spaces (e.g., recorded ambient weather) and building characteristics (e.g., dataset volume). Our experiments show that combining multiple source datasets under a zero-shot setup reduces the Mean Absolute Error (MAE) of the vanilla Transformer model by an average of 15.9 % for 24 h forecasts, compared to single-source baselines. Further fine-tuning these multi-source models with target-domain data yields an additional 3–5 % improvement. Notably, PatchTST outperforms the vanilla Transformer and Informer models. Overall, our results underscore the potential of combining Transformer architectures with TL techniques to enhance building energy consumption forecasting accuracy. However, careful selection of the TL strategy and attention to feature space compatibility are needed to maximize forecasting gains.
- ItemHyperspectral Data Can Classify Plant Functional Groups Within New Zealand Hill Farm Pasture(MDPI AG, 2025-03-21) Cushnahan T; Grafton M; Pearson D; Ramilan T; Hasenauer H
- ItemEnhancing Health and Exercise Consultation through Scenario-Based Learning: An Approach for Interpersonal Skill Development. A Practice Report(University of Southern Queensland Library, 2025-03-04) Cochrane DThis practice report describes the application of scenario-based learning to improve awareness of interpersonal skills in sport and exercise students. Thirty second-year undergraduate students over two consecutive academic years engaged in three scenario-based learning activities that simulated client interviews and consultations. The consensus among the students was that the scenarios increased their awareness of active listening, recognising physical client cues, and understanding the intricate dynamics of the ‘client-practitioner’ interaction. The implementation of scenario-based health and exercise consultations provided students with an immersive and effective learning experience, which promoted the development of the interpersonal skills required for successful client consultations.
- ItemFactors Affecting the Selection of Sustainable Construction Materials: A Study in New Zealand(MDPI (Basel, Switzerland), 2025-03-06) Bui T; Domingo N; Le AThe construction industry is increasingly prioritizing sustainability, with the selection of sustainable construction materials (SCMs) playing a crucial role in achieving environmental and regulatory objectives. However, New Zealand’s construction codes and sustainability standards lack cohesive, region-specific guidance, posing challenges for industry professionals in selecting appropriate materials. This study aims to identify the key factors influencing SCM selection within the New Zealand construction sector. An online questionnaire was distributed to 115 industry professionals, and data were analyzed using a structural equation modeling (SEM) with confirmatory factor analysis (CFA) to examine the relationships among social, economic, environmental, and technical factors. The finding was that technical factors are vital in achieving sustainable construction. Additionally, the social, economic, environmental, and technical factors were strongly correlated, affecting the selection of SCMs. Based on this research, construction consultants should advise customers on materials and the long-term economic benefits of investing in sustainable materials, which will cut operating expenses and environmental effects.
