Browsing by Author "Liu D"

Now showing 1 - 7 of 7
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    Blockchain-driven Supply Chain Resilience and Performance from a Relational Perspective: The Role of Inter-organizational Relationship and Systems Adaptability
    (Emerald Publlishing, 2025-10-04) Dou J; Tan N-L; Chung HFL; Liu D
    Purpose: This study investigates how blockchain technology enhances supply chain resilience and relationship performance by reinforcing inter-organizational relationships. Drawing on the relational view and boundary object perspective, we examine how blockchain influences relational trust and network capability, which in turn strengthen supply chain resilience and ultimately improve relationship performance. Design/methodology/approach: We proposed a conceptual framework and tested it using structural equation modeling (SEM) based on survey data collected from 251 manufacturing firms in China. Findings: The results indicate that blockchain technology significantly enhances supply chain resilience and improves relationship performance by fostering relational trust and network capability among supply chain partners. However, inter-organizational systems adaptability was found to negatively moderate the relationship between supply chain resilience and relationship performance, suggesting that system complexity may reduce the relational benefits of resilience. Originality: This study shifts the theoretical focus from the resource-based view to a relational perspective, providing new insights into how blockchain technology strengthens supply chain relationships to improve resilience and performance. It also challenges assumptions about technological adaptability by revealing that greater flexibility in inter-organizational systems may introduce coordination burdens that diminish relationship outcomes.
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    From COBIT to ISO 42001: Evaluating cybersecurity frameworks for opportunities, risks, and regulatory compliance in commercializing large language models
    (Elsevier B.V., 2024-09-01) McIntosh TR; Susnjak T; Liu T; Watters P; Xu D; Liu D; Nowrozy R; Halgamuge MN
    This study investigated the integration readiness of four predominant cybersecurity Governance, Risk and Compliance (GRC) frameworks – NIST CSF 2.0, COBIT 2019, ISO 27001:2022, and the latest ISO 42001:2023 – for the opportunities, risks, and regulatory compliance when adopting Large Language Models (LLMs), using qualitative content analysis and expert validation. Our analysis, with both LLMs and human experts in the loop, uncovered potential for LLM integration together with inadequacies in LLM risk oversight of those frameworks. Comparative gap analysis has highlighted that the new ISO 42001:2023, specifically designed for Artificial Intelligence (AI) management systems, provided most comprehensive facilitation for LLM opportunities, whereas COBIT 2019 aligned most closely with the European Union AI Act. Nonetheless, our findings suggested that all evaluated frameworks would benefit from enhancements to more effectively and more comprehensively address the multifaceted risks associated with LLMs, indicating a critical and time-sensitive need for their continuous evolution. We propose integrating human-expert-in-the-loop validation processes as crucial for enhancing cybersecurity frameworks to support secure and compliant LLM integration, and discuss implications for the continuous evolution of cybersecurity GRC frameworks to support the secure integration of LLMs.
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    From Google Gemini to OpenAI Q* (Q-Star): A Survey on Reshaping the Generative Artificial Intelligence (AI) Research Landscape
    (MDPI (Basel, Switzerland), 2025-02-01) McIntosh TR; Susnjak T; Liu T; Watters P; Xu D; Liu D; Halgamuge MN; Mladenov V
    This comprehensive survey explored the evolving landscape of generative Artificial Intelligence (AI), with a specific focus on the recent technological breakthroughs and the gathering advancements toward possible Artificial General Intelligence (AGI). It critically examined the current state and future trajectory of generative AI, exploring how innovations in developing actionable and multimodal AI agents with the ability scale their “thinking” in solving complex reasoning tasks are reshaping research priorities and applications across various domains, while the survey also offers an impact analysis on the generative AI research taxonomy. This work has assessed the computational challenges, scalability, and real-world implications of these technologies while highlighting their potential in driving significant progress in fields like healthcare, finance, and education. Our study also addressed the emerging academic challenges posed by the proliferation of both AI-themed and AI-generated preprints, examining their impact on the peer-review process and scholarly communication. The study highlighted the importance of incorporating ethical and human-centric methods in AI development, ensuring alignment with societal norms and welfare, and outlined a strategy for future AI research that focuses on a balanced and conscientious use of generative AI as its capabilities continue to scale.
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    Nitrogen decisions for cereal crops: a risky and personal business
    Farquharson R; Chen D; Yong L; Liu D; Ramilan T
    Cereal crops principally require Nitrogen (N) and water for growth. Fertiliser economics are important because of the cost at sowing with expectation of a financial return after harvest. The production economics framework can be used to develop information for ‘best’ fertiliser decisions. But the variability of yield responses for rainfed production systems means that fertiliser decisions are a risky business. How do farmers make such decisions, and can economics give any guidance? Simulated wheat yield responses to N fertiliser applications show tremendous variation between years or seasons. There are strong agronomic arguments for a Mitscherlich equation to represent yield responses. Plots of the 10th, 50th and 90th percentiles of yield response distributions show likely outcomes in ‘Poor’, ‘Medium’ and ‘Good’ seasons at four Australian locations. By adding the prices for Urea and wheat we predict that the ‘best’ decisions vary with location, soil, and (sometimes) season. We compare these predictions with typical grower fertiliser decisions. Australian wheat growers understand the yield responses in their own paddocks and the relative prices, so they are making relevant short-term fertiliser decisions. These are subjective or personal decisions. Myanmar smallholders grow rice and maize in the Central Dry Zone, with relatively low levels of fertiliser and low crop yields. They have pre-existing poverty, high borrowing costs and are averse to risky outcomes. A Marginal Rate of Return (MRR) analysis with a hurdle rate of 100% is illustrated for the Australian locations, and this approach will be tested in Myanmar.
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    Ransomware Reloaded: Re-examining Its Trend, Research and Mitigation in the Era of Data Exfiltration
    (Association for Computing Machinery New York, NY, United States, 2024-10-07) McIntosh T; Susnjak T; Liu T; Xu D; Watters P; Liu D; Hao Y; Ng A; Halgamuge M; Atienza D; Milano M
    Ransomware has grown to be a dominant cybersecurity threat by exfiltrating, encrypting, or destroying valuable user data and causing numerous disruptions to victims. The severity of the ransomware endemic has generated research interest from both the academia and the industry. However, many studies held stereotypical assumptions about ransomware, used unverified, outdated, and limited self-collected ransomware samples, and did not consider government strategies, industry guidelines, or cyber intelligence. We observed that ransomware no longer exists simply as an executable file or limits to encrypting files (data loss); data exfiltration (data breach) is the new norm, espionage is an emerging theme, and the industry is shifting focus from technical advancements to cyber governance and resilience. We created a ransomware innovation adoption curve, critically evaluated 212 academic studies published during 2020 and 2023, and cross-verified them against various government strategies, industry reports, and cyber intelligence on ransomware. We concluded that many studies were becoming irrelevant to the contemporary ransomware reality and called for the redirection of ransomware research to align with the continuous ransomware evolution in the industry. We proposed to address data exfiltration as priority over data encryption, to consider ransomware in a business-practical manner, and recommended research collaboration with the industry.
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    Using Remote and Proximal Sensing Data and Vine Vigor Parameters for Non-Destructive and Rapid Prediction of Grape Quality
    (MDPI AG, 2023-11-19) Lyu H; Grafton M; Ramilan T; Irwin M; Wei H-E; Sandoval E; Zhang C; Liu D
    The traditional method for determining wine grape total soluble solid (TSS) is destructive laboratory analysis, which is time consuming and expensive. In this study, we explore the potential of using different predictor variables from various advanced techniques to predict the grape TSS in a non-destructive and rapid way. Calculating Pearson’s correlation coefficient between the vegetation indices (VIs) obtained from UAV multispectral imagery and grape TSS resulted in a strong correlation between OSAVI and grape TSS with a coefficient of 0.64. Additionally, seven machine learning models including ridge regression and lasso regression, k-Nearest neighbor (KNN), support vector regression (SVR), random forest regression (RFR), extreme gradient boosting (XGBoost), and artificial neural network (ANN) are used to build the prediction models. The predictor variables include the unmanned aerial vehicles (UAV) derived VIs, and other ancillary variables including normalized difference vegetation index (NDVI_proximal) and soil electrical conductivity (ECa) measured by proximal sensors, elevation, slope, trunk circumference, and day of the year for each sampling date. When using 23 VIs and other ancillary variables as input variables, the results show that ensemble learning models (RFR, and XGBoost) outperform other regression models when predicting grape TSS, with the average of root mean square error (RMSE) of 1.19 and 1.2 ◦Brix, and coefficient of determination (R2 ) of 0.52 and 0.52, respectively, during the 20 times testing process. In addition, this study examines the prediction performance of using optimized soil adjusted vegetation index (OSAVI) or normalized green-blue difference index (NGBDI) as the main input for different machine learning models with other ancillary variables. When using OSAVI-based models, the best prediction model is RFR with an average R2 of 0.51 and RMSE of 1.19 ◦Brix, respectively. For NGBDI-based model, the RFR model showed the best average result of predicting TSS were a R2 of 0.54 and a RMSE of 1.16 ◦Brix, respectively. The approach proposed in this study provides an opportunity to grape growers to estimate the whole vineyard grape TSS in a non-destructive way.
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    Using Remote and Proximal Sensing Data and Vine Vigor Parameters for Non-Destructive and Rapid Prediction of Grape Quality
    (MDPI (Basel, Switzerland), 2023-11-19) Lyu H; Grafton M; Ramilan T; Irwin M; Wei H-E; Sandoval E; Zhang C; Liu D
    The traditional method for determining wine grape total soluble solid (TSS) is destructive laboratory analysis, which is time consuming and expensive. In this study, we explore the potential of using different predictor variables from various advanced techniques to predict the grape TSS in a non-destructive and rapid way. Calculating Pearson’s correlation coefficient between the vegetation indices (VIs) obtained from UAV multispectral imagery and grape TSS resulted in a strong correlation between OSAVI and grape TSS with a coefficient of 0.64. Additionally, seven machine learning models including ridge regression and lasso regression, k-Nearest neighbor (KNN), support vector regression (SVR), random forest regression (RFR), extreme gradient boosting (XGBoost), and artificial neural network (ANN) are used to build the prediction models. The predictor variables include the unmanned aerial vehicles (UAV) derived VIs, and other ancillary variables including normalized difference vegetation index (NDVI_proximal) and soil electrical conductivity (ECa) measured by proximal sensors, elevation, slope, trunk circumference, and day of the year for each sampling date. When using 23 VIs and other ancillary variables as input variables, the results show that ensemble learning models (RFR, and XGBoost) outperform other regression models when predicting grape TSS, with the average of root mean square error (RMSE) of 1.19 and 1.2 °Brix, and coefficient of determination (R2) of 0.52 and 0.52, respectively, during the 20 times testing process. In addition, this study examines the prediction performance of using optimized soil adjusted vegetation index (OSAVI) or normalized green-blue difference index (NGBDI) as the main input for different machine learning models with other ancillary variables. When using OSAVI-based models, the best prediction model is RFR with an average R2 of 0.51 and RMSE of 1.19 °Brix, respectively. For NGBDI-based model, the RFR model showed the best average result of predicting TSS were a R2 of 0.54 and a RMSE of 1.16 °Brix, respectively. The approach proposed in this study provides an opportunity to grape growers to estimate the whole vineyard grape TSS in a non-destructive way.