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The invasive golden apple snail Pomacea canaliculata has a strong reproductive capacity and has rapidly spread in Asian countries. Current control methods include physical, chemical, and biological approaches, but there has been limited research on the control of P. canaliculata in its different life stages. This study assessed the effectiveness of using giant river prawns Macrobrachium rosenbergii in controlling juveniles of P. canaliculata through a controlled indoor experiment. The density, size, and dispersal range of recently hatched juvenile snails were significantly lower among those kept with prawns than those kept without prawns, indicating a control effect of M. rosenbergii at least on P. canaliculata juveniles. Furthermore, the study speculates on the potential application of M. rosenbergii in the context of a rice–prawn symbiotic system of ecological farming to control invasive P. canaliculata. In terms of effectiveness and safety, its application might lead to a win-win situation for both rice-farm profits and the ecological benefits of invasive species control. [ABSTRACT FROM AUTHOR]

Quality-related prediction in the continuous-casting process is important for the quality and process control of casting slabs. As intelligent manufacturing technologies continue to evolve, numerous data-driven techniques have been available for industrial applications. This case study was aimed at developing a machine-learning algorithm, capable of predicting slag inclusion defects in continuous-casting slabs, based on process condition sensor data. A large dataset consisting of sensor data from nearly 7300 casting samples has been analyzed, with the empirical mode decomposition (EMD) algorithm utilized to process the multi-modal time series. The following machine-learning algorithms have been examined: K-Nearest neighbors, support vector classifier (linear and nonlinear kernels), decision trees, random forests, AdaBoost, and Artificial Neural Networks. Four over-sampling or under-sampling algorithms have been adopted to solve imbalanced data distribution. In the experiment, the optimized random forest outperformed other machine-learning algorithms in terms of recall and ROC AUC, which could provide valuable insights for quality control. [ABSTRACT FROM AUTHOR]

Federated learning has attracted much attention in fault diagnosis since it can effectively protect data privacy. However, efficient fault diagnosis performance relies on the uninterrupted training of model parameters with massive amounts of perfect data. To solve the problems of model training difficulty and parameter negative transfer caused by data corruption, a novel cross-device fault diagnosis method based on repaired data is proposed. Specifically, the local model training link in each source client performs random forest regression fitting on the fault samples with missing fragments, and then the repaired data is used for network training. To avoid inpainting fragments to produce the wrong characteristics of faulty samples, joint domain discrepancy loss is introduced to correct the phenomenon of parameter bias during local model training. Considering the randomness of the overall performance change brought about by the local model update, an adaptive update is proposed for each round of global model download and local model update. Finally, the experimental verification was carried out in various industrial scenarios established by three sets of bearing data sets, and the effectiveness of the proposed method in terms of fault diagnosis performance and data privacy protection was verified by comparison with various currently popular federated transfer learning methods. [ABSTRACT FROM AUTHOR]

Direct combustion of solid biomass fuel is one of the most common energy sources in developing countries. Evaluation of technology for household biomass pellet fuel combustion is critical, since promoting poorly designed devices may have risks due to exposure to high levels of emissions. This study evaluated the effects of various testing conditions on a top-lit forced-up-draft semi-gasifier cooking stove. An orthogonal test was designed with different fuel masses, chamber heights, air supply rates, and ending points. The investigation showed that using forced secondary air and more fuel tended to improve both thermal and gas emissions performance. The ending points did not have significant effects on thermal efficiency or the carbon dioxide emission factor, but did affect particulate matter emission. A relatively lower chamber height demonstrated better performance on thermal metrics. However, a taller flame had better performance on particulate matter emission factors. The results of the indicators reported by different bases, such as fuel mass-based or useful energy-based were also quite different. The study showed that different testing conditions had significant effects on combustion performances. Testing sequences and emission factors should be reviewed and defined clearly when forming testing methods and standards for biomass pellet fuel combustion. [ABSTRACT FROM AUTHOR]

This study proposes an approach to minimize the maximum makespan of the integrated scheduling problem in flexible job-shop environments, taking into account conflict-free routing problems. A hybrid genetic algorithm is developed for production scheduling, and the optimal ranges of crossover and mutation probabilities are also discussed. The study applies the proposed algorithm to 82 test problems and demonstrates its superior performance over the Sliding Time Window (STW) heuristic proposed by Bilge and the Genetic Algorithm proposed by Ulusoy (UGA). For conflict-free routing problems of Automated Guided Vehicles (AGVs), the genetic algorithm based on AGV coding is used to study the AGV scheduling problem, and specific solutions are proposed to solve different conflicts. In addition, sensors on the AGVs provide real-time data to ensure that the AGVs can navigate through the environment safely and efficiently without causing any conflicts or collisions with other AGVs or objects in the environment. The Dijkstra algorithm based on a time window is used to calculate the shortest paths for all AGVs. Empirical evidence on the feasibility of the proposed approach is presented in a study of a real flexible job-shop. This approach can provide a highly efficient and accurate scheduling method for manufacturing enterprises. [ABSTRACT FROM AUTHOR]

The Xinjiazui gold deposit, a newly discovered deposit, is situated in the northwestern margin of the Yangtze Block, China. The source and genesis of gold mineralization are poorly understood. It is urgent to use the H–O isotopic composition of quartz and geochemistry of pyrite to evaluate the origins of the Au and ore-forming fluids of this deposit. Three types of pyrite were identified, including synsedimentary framboidal pyrites (Py0), the directional arrangement of pyrites in pre-mineralization stage (Py1), and euhedral coarse-grain pyrites in the quartz–sulfide veins of the mineralization stage (Py2). The As content in Py2 is relatively higher than Py0 and Py1, indicating that the ore-forming fluids are strongly enriched in As. The δ34S values of Py2 (+5.50–+13.34‰) overlap with the S1–2M phyllite (+7.25‰–+8.70‰). This result is consistent with the Pb isotopic composition of Py2, showing that the source of ore-forming materials was derived from the S1–2M phyllite. Meanwhile, the variations in quartz's H and O isotopic composition suggest that the ore-forming fluids were derived originally from metamorphic fluid. Additionally, the Au mineralization is strictly controlled by the shear zone. Above all, we would like to classify the Xinjiazui deposit as an orogenic gold deposit. [ABSTRACT FROM AUTHOR]