Long-term, ambitious commitments and stable regulatory policies have established renewable energy as a major preference in the Australian energy sector. Given that energy grids are among the most complex machines and require immediate decisions to be made in real-time, artificial intelligence (AI) technologies are necessary and viable options. This review aims to assess the adoption of AI technologies in improving the power management, maintenance, and control of renewable energy systems (RES) in Australia, to support the realization of net-zero emissions by 2050. To achieve the aims of this review, previous studies from 2000 to 2024 are collected from various databases and analyzed. This review found that AI technologies have contributed to improved forecasting accuracy, operational efficiency, cost savings and emissions reduction in RES in Australia. Findings show that renewable energy generation in Australia has been steadily growing, and the successful integration of AI into Australia's RES depends on the use of well-defined, multi-dimensional metrics that encompass technical performance, economic impact, environmental sustainability, and system reliability.

This paper presents the development of a multicriteria group decision making method for evaluating the performance of quantum computing technologies. The subjectiveness and imprecision of the decision making process is dealt effectively with the use of interval-valued intuitionistic fuzzy numbers. A multicriteria group decision making algorithm is developed for generating an overall intuitionistic fuzzy performance value for every quantum computing technology alternative across all criteria. An example is presented that shows the multicriteria group decision making method is efficient and effective for dealing with the performance quantum computing technology evaluation problem.

The identification and classification of plant diseases is challenging due to the complexity and variability of symptoms across different species, and the need for timely and accurate diagnosis to effective disease management. However, the existing methods of diagnosing plant diseases often require extensive expert knowledge and can be labor-intensive and time-consuming. This paper presents a novel method leveraging a dynamic self-attention mechanism within convolutional neural networks to enhance the classification accuracy of plant diseases. By allowing the mechanism to focus adaptively on the most informative parts of the input images, this method successfully detects the complex patterns and relationships within the disease symptoms. This method is evaluated by incorporating it in a model that specifically leverages the strengths of EfficientNet architecture combined with the Scaled Exponential Linear Unit (SeLU) activation function on grape leaves dataset containing various types of diseases. This model demonstrates superior performance in detecting a variety of plant diseases, surpassing existing baseline convolutional network methods in both speed and accuracy. This research not only advances the field of plant disease management with cutting-edge AI techniques but also offers a scalable and efficient tool for agriculture practitioners to combat plant diseases more effectively.

This paper examines the impact of consumer recycling behavior and intention to participate in e-waste recycling scheme for achieving sustainable e-waste management practices in Australia. To accomplish this goal, the “theory of planned behavior” (TPB), “theory of altruism and consumer behavior” (TACB) and “sustainability theory” (ST) are adopted into the main research framework. An online survey questionnaire will be used for collecting the required data. From the theoretical perspective, this study will contribute to the “body of knowledge” on formal recycling, treatment and disposal of discarded e-waste through identifying factors that influence consumers’ behavioral intentions towards e-waste. From a practical perspective, this study will provide insights to environmentalists, researchers and governments in developing appropriate recycling strategies for addressing the e-waste problem

The increasing use of digital technologies leads to more contingent, flexible, and distributed work arrangements between individuals and organizations, resulting in wide adoption of digital work. This makes understanding the impact of digital technologies on job performance critical. This study investigates the affordance of digital technologies for enhancing job performance in digital work. A technology affordance theory-based model is tested and validated through structural equation modeling of the collected survey data. The study shows that using digital technologies can enhance job performance through improved coordination, communication, knowledge sharing, and decision-making. This study is the first in applying the technology affordance theory for exploring the role of digital technologies in job performance in digital work. The findings can lead to better formulated strategies and policies to facilitate digital technology use.

Despite the growing attention paid to conversational chatbots, there is a lack of research in either offering culturally related empathetic strategies when English as additional language learners encounter emotional discomfort or simulating a sense of humour in language acquisition. This paper aims to propose a multivariate normal distribution model for cross-cultural class, BERT for humour detection and response, and NRC VAD to compute empathetic intensity values. The novel algorithm will contribute to significant and long-term outcomes of language learners when exposed to new cultural, cognitive and psychological experiences.

Trust is a crucial factor in technology adoption decisions made by organizations. Understanding its importance is critical for accelerating the adoption of financial technologies (FinTech). Earlier studies investigated the significance of trust in new technologies and the subsequent technology adoption decisions. However, these were limited to studying the benefits of FinTech, and not the factors enabling trust in FinTech and the subsequent adoption decision. This research-in-progress paper aims to investigate the role of organizational factors in enabling trust in FinTech to support FinTech adoption among Indian financial organizations. This study adopts a quantitative method for data collection. The initial data collected from Indian financial organizations indicate the importance of senior management support, organization size and competence in enabling trust in FinTech. The role of organizational factors in enabling trust in FinTech and the subsequent adoption decision will be then assessed using the structural model.

Exchange of information, financial transactions, and involvement of intermediaries play an important role in agriculture. However, current ICT-based agriculture systems are centralized and fail to address problems such as information asymmetry and reliability. Besides, consumers are keen to know the origin of food (traceability), intermediaries focus on transparency of shareable data and tracking of transactions are the areas to be studied in depth for smart and sustainable farming. Disruptive technology like blockchain is suitable to effectively manage smart farming. Blockchain promises reliability and authenticity of source of information as it can track the origin of a transaction. Several researchers have proposed conceptual frameworks and models. However, there is a limited study on real-time architecture that uses blockchain and decentralized applications to address the issues for smart farming. This research in progress paper presents the current gaps and focuses on incorporating blockchain to a smart and sustainable farming by developing an effective architecture. The research outcomes of the proposed study will benefit the agriculture value chains, intermediaries, farmers, and food suppliers in Australia.

Students studying computer networking often have difficulty with relating theoretical models like TCP/IP with actual components. The use of illustrations and diagrams are only partially successful in helping students to grasp what happens `inside' networking equipment such as routers. Virtual Reality (VR) provides a unique opportunity for students to step inside a router and physically simulate the routing process, helping to build a more solid mental model. This paper presents the design of a novel virtual reality system for teaching elements of network routing. Through providing a physical simulation of network address calculation, it aims to improve student conceptual learning and retention.

This paper presents a multicriteria analysis approach for measuring and benchmarking open innovation practices of IT organizations. Five important criteria for measuring the open innovation performance of IT organizations are identified. To model the vagueness and imprecision of the decision making process, interval-valued intuitionistic fuzzy numbers are used. Based on the concept of the ideal solutions, the closeness coefficient can be obtained for determining the ranking of the alternatives. This multicriteria analysis approach helps IT organizations to understand their strengths and weaknesses in terms of their open innovation practices performance, and identify relevant areas for improvement.

This paper presents a multicriteria decision making approach for evaluating the performance of recoverable end-of-life products in the reverse supply chain. The multidimensional nature of the performance evaluation process is handled in the context of multicriteria analysis. Linguistic terms approximated by triangular fuzzy numbers are used to tackle the subjectiveness and imprecision of the performance evaluation process. An efficient algorithm is developed for producing an overall performance index for every recoverable end-of-life product alternative across all performance evaluation criteria. An example is presented for demonstrating the applicability of the approach.

Nutrition related diseases such as Cardiovascular Diseases, type 2 diabetes, and certain types of cancers are widely prevalent, and increasing in both developed and developing countries. This escalates the need to consistently follow a nutritionally balanced diet. Formulation of nutritionally balanced Meals is a complex task, which requires considerable effort, time, and intellectual capability for evaluation of Menu items and analysis of a range of nutrient values. As a step towards automating this task, this interdisciplinary study presents a fuzzy optimization model applying Fuzzy Multi-objective Linear Programming (FMOLP) to compose a daily diet consisting of nutritionally balanced Meals which limit nutrients such as cholesterol, sugar, saturated fat, and sodium. An effective algorithm is developed to create a pare to optimal solution set of optimized daily diet choices. The model is tested using one hundred Menu items, for demonstrating the applicability of the model in composing a nutritionally balanced diet.