In the era of Industry 4.0, digital agriculture is developing very rapidly and has achieved considerable results. Nowadays, digital agriculture-based research is more focused on the use of robotic fruit picking technology, and the main research direction of such topics is algorithms for computer vision. However, when computer vision algorithms successfully locate the target object, it is still necessary to use robotic arm movement to reach the object at the physical level, but such path planning has received minimal attention. Based on this research deficiency, we propose to use Unity software as a digital twin platform to plan the robotic arm path and use ML-Agent plug-in as a reinforcement learning means to train the robotic arm path, to improve the accuracy of the robotic arm to reach the fruit, and happily the effect of this method is much improved than the traditional method.

Nowadays, cluster tools are extensively used for many wafer manufacturing processes, such as coating, lithograph, developing, etching, deposition, and testing. Traditional process modules in cluster tools can execute a single operation only. With the rapid development of equipment design, multifunctional process modules (MPMs) are equipped to serve for processing multiple operations together just like a single operation. With different wafer processing parameters, MPMs may be set for processing multiple operations together or processing just a single operation to form different schedules so as to maximize the productivity. Thus, it is highly desired to find an efficient scheduling method to quickly adapt to wafer processing parameter changes for productivity maximization by taking the advantages of MPMs. To tackle this issue, a deadlock-free Petri net (PN) model is developed to describe the behavior of a single-arm cluster tool. Based on the evolving mechanism of the PN model, two algorithms are developed to calculate the makespan for completing a given number of wafers. Then, an adaptive scheduling method is presented to set the functions of MPMs to minimize the makespan. Finally, experimental results show the efficiency and effectiveness of the proposed method.

The estimation of disc cutter life is important due to the high equipment replacement cost in tunnel boring machines (TBMs). In this paper, we propose a comprehensive method for predicting disc cutter wear, integrating dimensionality reduction in shield tunneling parameters, data input preprocessing, and optimized model selection. First, we select core TBM operation parameters as model inputs through principal component analysis (PCA) and PCA directly. And, then, based on the characteristics of the operation parameters, we propose a data preprocessing method for time series models. This method uses excavation rings as units, providing a more accurate representation of the actual shield excavation process. Furthermore, by evaluating the prediction accuracy, training efficiency, and inference efficiency of various models, we demonstrate that the combined PCA data + Min Max Scaler + gated recurrent unit (GRU) method achieves the most accurate prediction results while offering real-time prediction capabilities.

The disposal of orchard garbage (including pruning branches, fallen leaves, and non-biodegradable materials such as pesticide containers and plastic film) poses major difficulties for horticultural production and soil sustainability. Unlike general agricultural garbage, orchard garbage often contains both biodegradable organic matter and hazardous pollutants, which complicates efficient recycling. Traditional manual sorting methods are labour-intensive and inefficient in large-scale operations. To this end, we propose a lightweight YOLOv7-based detection model tailored for the orchard environment. By replacing the CSPDarknet53 backbone with MobileNetV3 and GhostNet, an average accuracy (mAP) of 84.4% is achieved, while the computational load of the original model is only 16%. Meanwhile, a supervised comparative learning strategy further strengthens feature discrimination between horticulturally relevant categories and can distinguish compost pruning residues from toxic materials. Experiments on a dataset containing 16 orchard-specific garbage types (e.g., pineapple shells, plastic mulch, and fertiliser bags) show that the model has high classification accuracy, especially for materials commonly found in tropical orchards. The lightweight nature of the algorithm allows for real-time deployment on edge devices such as drones or robotic platforms, and future integration with robotic arms for automated collection and sorting. By converting garbage into a compostable resource and separating contaminants, the technology is aligned with the country’s garbage segregation initiatives and global sustainability goals, providing a scalable pathway to reconcile ecological preservation and horticultural efficiency.

Semiconductor manufacturing relies on a long and complex production line for wafer fabrication. In a modern semiconductor fabrication plant (fab), a wafer manufacturing line is composed of more than 1,000 manufacturing tools and an overhead hoist system for delivering wafer lots among the tools. Novel technologies, such as Internet of Things (IoT) and software engineering technologies, are adopted in semiconductor manufacturing such that large amounts of data can be collected from manufacturing tools. Also, these tools and the overhead hoist system should cooperate well with each other. Due to the data-processing capacity limitation in a current fault detection and classification (FDC) system, the diagnosis efficiency is limited such that some undesired events cannot be detected in time, leading to significant economic loss. With the booming development of big data technology, this work conducts a study on a new FDC framework based on a Hadoop ecosystem to deal with the data-processing limitation and improve diagnosis efficiency. Also, a migration path is presented such that the current FDC system can be smoothly migrated to a Hadoop ecosystem-based one without shutting down a wafer fabrication line. Experimental results show that the proposed FDC framework can run safely and stably.