Accurate short-term forecasting of hauling-fleet capacity is crucial in open-pit mining, where weather fluctuations, mechanical breakdowns, and variable crew availability introduce significant operational uncertainties. We propose a deep-learning framework that blends real-world operational records (high-resolution rainfall measurements, fleet performance telemetry) with synthetically generated mechanical-breakdown scenarios to enable the model to capture fluctuating high-impact failure events. We evaluate two architectures: an XGBoost regressor achieving a median absolute error (MedAE) of 14.3 per cent and a Long Short-Term Memory network with a MedAE of 15.1 per cent. Shapley Additive exPlanations (SHAP) value analyses identify cumulative rainfall, historical payload trends, and simulated breakdown frequencies as dominant predictors. Integration of simulated breakdown data and shift-planning features notably reduces prediction volatility. Future work will further integrate maintenance-scheduling indicators (Mean Time Between Failures, Mean Time to Repair), detailed human resource data (operator absenteeism, crew efficiency metrics), blast event scheduling, and other operational constraints to enhance forecast robustness and adaptability. This hybrid modelling approach offers a comprehensive decision-support tool for proactive, data-driven fleet management under dynamically uncertain conditions.

Generative AIBIM, a successful structural design pipeline, has proven its ability to intelligently generate high-quality, diverse, and creative shear wall designs that are tailored to specific physical conditions. However, the current module of Generative AIBIM that generates designs, known as the physics-based conditional diffusion model (PCDM), necessitates 1000 iterations for each generation due to its reliance on the denoising diffusion probabilistic model (DDPM) sampling process. This leads to a time-consuming and computationally demanding generation process. To address this issue, this study introduces the denoising diffusion implicit model (DDIM), an accelerated generation method that replaces the DDPM sampling process in PCDM. While the original DDIM was designed for DDPM and the optimization process of PCDM differs from that of DDPM, this paper designs "DDIM sampling for PCDM," which modifies the original DDIM formulations to adapt to the optimization process of PCDM. Experimental results demonstrate that DDIM sampling for PCDM can accelerate the generation process of the original PCDM by a factor of 100 while maintaining the same visual quality in the generated results. This study effectively showcases the effectiveness of DDIM sampling for PCDM in expediting intelligent structural design. Furthermore, this paper reorganizes the contents of DDIM, focusing on the practical usage of DDIM. This change is particularly meaningful for researchers who may not possess a strong background in machine learning theory but are interested in utilizing the tool effectively.

Even for a conservative estimate, 80% of enterprise data reside in unstructured files, stored in data lakes that accommodate heterogeneous formats. Classical search engines can no longer meet information seeking needs, especially when the task is to browse and explore for insight formulation. In other words, there are no obvious search keywords to use. Knowledge graphs, due to their natural visual appeals that reduce the human cognitive load, become the winning candidate for heterogeneous data integration and knowledge representation. In this paper, we introduce Docs2KG, a novel framework designed to extract multimodal information from diverse and heterogeneous unstructured documents, including emails, web pages, PDF files, and Excel files. Dynamically generates a unified knowledge graph that represents the extracted key information, Docs2KG enables efficient querying and exploration of document data lakes. Unlike existing approaches that focus on domain-specific data sources or pre-designed schemas, Docs2KG offers a flexible and extensible solution that can adapt to various document structures and content types. The proposed framework unifies data processing supporting a multitude of downstream tasks with improved domain interpretability. Docs2KG is publicly accessible at https://docs2kg.ai4wa.com, and a demonstration video is available at https://docs2kg.ai4wa.com/Video.

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Officials had to close Mullaloo Beach in Perth, Western Australia, for the second time in a week on Monday, January 9, after a tiger shark was spotted swimming close to the shore. Check out this video, taken from a drone. A 16-year-old girl died after a shark mauled her while swimming in the Swan River in Australia, with only a teen diving in to save her as others watched in horror. "A female received injuries after being bitten by an unknown species of shark at approximately 3.35pm on Feb. 4 2023," the Department of Primary Industries and Regional Development (DPIRD) said of the incident. "DPIRD is working with WA Police and local authorities to coordinate responses. A DPIRD Fisheries vessel is on the water monitoring the area, and DPIRD officers are conducting land-based patrols."

Fugro has officially opened the Australian Space Automation, Artificial Intelligence and Robotics Control Complex, better known as SpAARC. Located in Perth's central business district, this new world-class facility is a joint initiative by the Australian Space Agency, the Western Australia (WA) government, and Fugro.

When Lauren Dry heard last year that facial recognition cameras were being trialled in the suburb of East Perth, she thought it was a joke. "I just thought to myself: What do you mean facial recognition cameras, that's sci-fi! That doesn't happen in Perth," she told 7.30. "And I looked into it and I was, like, this is real." Ms Dry enjoys a quiet life at home with her young family in Perth's leafy suburbs.

Perth City Council has reportedly been filming and tracking people moving around parts of the city without their knowledge. In what the council calls a trial, a network of 30 cameras with facial recognition technology have been deployed across East Perth. This has quietly gone on for six months. The cameras use deep-learning artificial intelligence (AI) to recognise faces and vehicles, and to count passing people – a form of population control which China widely employs, and is criticised for by human rights groups. But in Perth – the third Australian city to invest in the technology – many residents were unaware of the trial before it started.

One of the anxieties plaguing the work force as we enter the next decade is the fear of automation replacing blue collar jobs. At the forefront of these anxieties is artificial intelligence (AI). The discussion is even such a hot-pressed issue that American Democratic candidate Andrew Yang's argument for a universal basic income policy is riding on its ability to speak to middle-American manual labour workers. However, for a lot of consumers around the world, the thought of automation can be exciting -- such as letting a Tesla drive you hands free down the freeway on a long trip. But this doesn't create an exception for those that are worried their jobs will be replaced by an automated crane or self-driven truck.

If a picture is worth a thousand words, but still missing valuable location data, then why not use artificial intelligence (AI) and machine learning (ML) to fill in the gaps? This graphic below shows vast data sets containing buildings, green spaces, roads to travel, and parking lots. Drill down even further and see rooftops, solar panels, fire hydrants, gas lines, and many other objects. And all this data constantly changes over time. City residents move, purchase new developments for their homes, drive roads with new potholes, and build new construction.