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Hermit crabs associated with catches from the eastern rock lobster (Sagmariasus verreauxi) fishery along the coast of NSW, Australia
In New South Wales (NSW), Australia, the majority of the commercial catch of eastern rock lobster (Sagmariasus verreauxi) is captured from traps fished on the mid and outer continental shelf in depths 50 – 220 m (119.5 t in 2021–22: 66.5 % of landings). Hermit crabs are the greatest bycatch from this fishery. The Fishery Management Strategy (2007) for the NSW lobster fishery, recognised the need to quantify by-catch species associated with lobster catches with an emphasis on increasing knowledge of the populations of hermit crabs along the NSW coast. An observer-based survey during 2008 and 2009 quantified by-catch from the fishery including, for each hermit crab species identified: (i) spatial and temporal distribution; (ii) relative abundance (number per trap-lift), and (iii) size distribution (shield length, SL). A total of 5782 hermit crabs were collected from 70 offshore trips comprising 722 trap-lifts. No hermit crabs were collected from 73 inshore trips (< 50 m depth) comprising 3232 trap-lifts, due to the low number (seven) of hermit crabs observed. Five species of hermit crab were identified. Three species were captured in very low numbers: Dardanus crassimanus (n=2), Dardanus pedunculatus (n=2) and Dardanus australis (n=1). The striated hermit crab (Dardanus arrosor; Herbst, 1796; n = 1970) and the stridulating hermit crab (Strigopagurus strigimanus; White, 1847; n =3812) were common in all latitudinal zones (30° - 37°S) and offshore depths (50–220 m) sampled. Abundance of D. arrosor decreased southward in contrast to S. strigimanus that showed the opposite pattern. Both species were more abundant on the outer-shelf than the mid-shelf. Mean SL of males was greater than females for both species across all latitudes on both the mid- and outer-shelf. Annual catches by the commercial fishery, by latitude and depth, were estimated for each species. This research provides a baseline for monitoring and interpretation of any future changes in the distribution and abundance of hermit crab species along the NSW coast.
Geopolitics and the changing landscape of global value chains and competition in the global semiconductor industry: Rivalry and catch-up in chip manufacturing in East Asia
This paper examines the changing landscape of GVCs and competition in the global semiconductor industry in the context of new geopolitics featured by the United States implementing “chokepoint” measures to limit the rise of semiconductor manufacturing in China. Overall, the paper finds that these US measures, like the IRA and CHIPS act, will have important impacts on semiconductor GVCs, especially in three types of memory (HBM, DRAM and NAND) and logic chips, and will slow down the speed and process of China's catching up and possibility of leapfrogging. By developing a conceptual framework for analyzing realism-based great power rivalries and national firm responses, we note that lead firms in South Korea and Taiwan can muddle through by reconfiguring their modes of GVCs, which can be summarized as “a bigger capacity and higher-ends in home bases and a smaller capacity and lower-ends abroad.” Analyses of US patents show that Korea and Taiwan have maintained their technological superiority in terms of both quantity and quality of their patents, compared to China, whereas Japan has lost its past superiority to China at least in patent quantity. We also find that the pace of China's catch-up is very fast in quantity, but slow in quality in key segments (DRAM, NAND and logic chips), except HBM which is the most recent segment where China has already surpassed Korea or Taiwan in terms of the number of patents. Whereas China has been catching up rapidly in the number of patents, it might encounter problems in turning that into market catch-up given the existing restrictions in accessing complementary technologies and chipmaking equipment, such as advanced lithography machines (EUV) or even more matured technologies (DUV), and software. Severely constrained by these technological entry barriers, the degree of catching up by China tends to be faster in lower-end products by foundry firms (e.g. SMIC), medium to high in NAND memory chips (e.g. YTMC), and slow or difficult in DRAM (e.g. CXMT). In the meantime, China has been making progress in domesticating value chains in diverse equipment and components in chip manufacturing.
A novel jujube tree trunk and branch salient object detection method for catch-and-shake robotic visual perception
Visual perception has become a prerequisite for automated jujube harvesting robot operations under complex orchard conditions. Catch-and-Shake harvesting, as the most efficient and common harvesting method, has widely been applied on various manually operated harvesters to complete large-area jujube fruit harvesting. However, the main factors restricting the development of existing harvesters are labor shortage, high labor cost, and low operating efficiency. To address the issues, we designed a catch-and-shake harvesting robot for jujube tree trunks and branches visual perception that can provide a barrier-free catch-and-shake operation area and guide the manipulator to reach the area to complete the harvesting operation. Meanwhile, a visual perception system including tree trunks and branches detection, skeleton extraction, catch-and-shake area confirmation was presented to guide robot intelligent operations. In the visual perception system, a novel salientobjectdetectionmodel called feature intersection and fusion Transformer (FIT-Transformer) network was proposed to split branches and background to provide reference for determining safe catch-and-shake areas. Moreover, we designed a diverse feature aggregation (DFA) and an attention feature fusion module (AFFM) to strengthen feature learning capabilities and obtain robust perception models. Comparative experimental results showed that our proposed FIT-Transformer model outperformed 12 state-of-the-art (SOTA) algorithms including C2FNet, RAS, BASNet, U2Net, SCRNet, PiCANet, EDRNet, EGNet, ICONR, VST, TransSOD and ABiU_Net. Specifically, the segmentation accuracy of jujube tree trunks and branches using our method showed the satisfactory result on five evaluation indexes under natural environment (the EM, SM, WF, FM and MAE reached 0.9713, 0.8991, 0.8854, 0.8905, and 0.0302, respectively). Field experiments also proved that our method could meet the requirements of operational accuracy and real-time operations.
PaLM: Point Cloud and Large Pre-trained Model Catch Mixed-type Wafer Defect Pattern Recognition
As the technology node scales down to 5nml3nm, the consequent difficulty has been widely lamented. The defects on the surface of wafers are much more prone to emerge during manufacturing than ever. What's worse, various single-type defect patterns may be coupled on a wafer and thus shape a mixed-type pattern. To improve yield during the design cycle, mixed-type wafer defect pattern recognition is required to perform to identify the failure mechanisms. Based on these issues, we revisit failure dies on wafer maps by treating them as point sets in two-dimensional space and propose a two-stage classification framework, PoLM. The challenge of noise reduction is considerably improved by first using an adaptive alpha-shapes algorithm to extract intricate geometric features of mixed-type patterns. Unlike sophisticated frameworks based on CNNs or Transformers, PoLM only completes classification within a point cloud cluster for aggregating and dispatching features. Furthermore, recognizing the remarkable success of large pre-trained foundation models (e.g., OpenAI's GPT-n series) in various visual tasks, this paper also introduces a training paradigm leveraging these pre-trained models and fine-tuning to improve the final recognition. Experiments demonstrate that our proposed framework significantly surpasses the state-of-the-art methodologies in classifying mixed-type wafer defect patterns.