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The accurate segmentation and quantification of retinal fluid in Optical Coherence Tomography (OCT) images are crucial for the diagnosis and treatment of ophthalmic diseases such as age-related macular degeneration. However, the accurate segmentation of retinal fluid is challenging due to significant variations in the size, position, and shape of fluid, as well as their complex, curved boundaries. To address these challenges, we propose a novel multi-scale feature fusion attention network (FNeXter), based on ConvNeXt and Transformer, for OCT fluid segmentation. In FNeXter, we introduce a novel global multi-scale hybrid encoder module that integrates ConvNeXt, Transformer, and region-aware spatial attention. This module can capture long-range dependencies and non-local similarities while also focusing on local features. Moreover, this module possesses the spatial region-aware capabilities, enabling it to adaptively focus on the lesions regions. Additionally, we propose a novel self-adaptive multi-scale feature fusion attention module to enhance the skip connections between the encoder and the decoder. The inclusion of this module elevates the model's capacity to learn global features and multi-scale contextual information effectively. Finally, we conduct comprehensive experiments to evaluate the performance of the proposed FNeXter. Experimental results demonstrate that our proposed approach outperforms other state-of-the-art methods in the task of fluid segmentation. [ABSTRACT FROM AUTHOR]

It is of practical significance to evaluate whether enhancing outward foreign direct investment (OFDI) can promote carbon mitigation under the new urbanization background toward China's sustainable development and carbon neutrality. The impact of OFDI on carbon emissions is investigated from the dual perspectives of the urbanization threshold and the mediating path by using panel data from China's 30 provinces during the period of 2003–2015 and considering both population and land. The results show that there is a significant impact from interprovincial OFDI on CO2 emissions with the double threshold effect of urbanization, and that OFDI expansion will increase CO2 emissions with urbanization; however, the different stages of urbanization show inverted U-shaped characteristics that first rise and then fall. The optimization of industrial structures has not passed the mediating effects test during the sample period, while the rationalization of the industrial structure provides a mediating effect in the primary stage of urbanization and a suppressing effect in the high level stage of urbanization. Import dependence only shows a masking effect in the intermediate stage of urbanization, while the technical level shows an intermediary effect in the primary stage of urbanization and a masking effect in the intermediate stage. The intensification of OFDI has brought different effects on economic and social production in various regions of China under urbanization, which has further affected regional carbon emissions. Discussing these effects would help to provide constructive suggestions for the regional coordination of development, new urbanization construction and urban low carbon transformation. [ABSTRACT FROM AUTHOR]

Large-scale measurement plays an increasingly important role in intelligent manufacturing. However, existing instruments have problems with immersive experiences. In this paper, an immersive positioning and measuring method based on augmented reality is introduced. An inside-out vision measurement approach using a multi-camera rig with non-overlapping views is presented for dynamic six-degree-of-freedom measurement. By using active LED markers, a flexible and robust solution is delivered to deal with complex manufacturing sites. The space resection adjustment principle is addressed and measurement errors are simulated. The improved Nearest Neighbor method is employed for feature correspondence. The proposed tracking method is verified by experiments and results with good performance are obtained. [ABSTRACT FROM AUTHOR]

The objective of this study was to use DRIFT spectroscopy with uni- and multivariate molecular spectral analyses as a novel approach to detect molecular features of spectra mainly associated with carbohydrate in the co-products (wheat DDGS, corn DDGS, blend DDGS) from bioethanol processing in comparison with original feedstock (wheat (Triticum), corn (Zea mays)). The carbohydrates related molecular spectral bands included: A&#95;Cell (structural carbohydrates, peaks area region and baseline: ca. 1485-1188 cm(-1)), A&#95;1240 (structural carbohydrates, peak area centered at ca. 1240 cm(-1) with region and baseline: ca. 1292-1198 cm(-1)), A&#95;CHO (total carbohydrates, peaks region and baseline: ca. 1187-950 cm(-1)), A&#95;928 (non-structural carbohydrates, peak area centered at ca. 928 cm(-1) with region and baseline: ca. 952-910 cm(-1)), A&#95;860 (non-structural carbohydrates, peak area centered at ca. 860 cm(-1) with region and baseline: ca. 880-827 cm(-1)), H&#95;1415 (structural carbohydrate, peak height centered at ca. 1415 cm(-1) with baseline: ca. 1485-1188 cm(-1)), H&#95;1370 (structural carbohydrate, peak height at ca. 1370 cm(-1) with a baseline: ca. 1485-1188 cm(-1)). The study shows that the grains had lower spectral intensity (KM Unit) of the cellulosic compounds of A&#95;1240 (8.5 vs. 36.6, P < 0.05), higher (P < 0.05) intensities of the non-structural carbohydrate of A&#95;928 (17.3 vs. 2.0) and A&#95;860 (20.7 vs. 7.6) than their co-products from bioethanol processing. There were no differences (P > 0.05) in the peak area intensities of A&#95;Cell (structural CHO) at 1292-1198 cm(-1) and A&#95;CHO (total CHO) at 1187-950 cm(-1) with average molecular infrared intensity KM unit of 226.8 and 508.1, respectively. There were no differences (P > 0.05) in the peak height intensities of H&#95;1415 and H&#95;1370 (structural CHOs) with average intensities 1.35 and 1.15, respectively. The multivariate molecular spectral analyses were able to discriminate and classify between the corn and corn DDGS molecular spectra, but not wheat and wheat DDGS. This study indicated that the bioethanol processing changes carbohydrate molecular structural profiles, compared with the original grains. However, the sensitivities of different types of carbohydrates and different grains (corn and wheat) to the processing differ. In general, the bioethanol processing increases the molecular spectral intensities for the structural carbohydrates and decreases the intensities for the non-structural carbohydrates. Further study is needed to quantify carbohydrate related molecular spectral features of the bioethanol co-products in relation to nutrient supply and availability of carbohydrates.