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Coherent optics has profoundly impacted diverse applications ranging from communications, LiDAR to quantum computations. However, developing coherent systems in integrated photonics comes at great expense in hardware integration and energy efficiency. Here we demonstrate a high-coherence parallelization strategy for advanced integrated coherent systems at minimal cost. By using a self-injection locked microcomb to injection lock distributed feedback lasers, we achieve a record high on-chip gain of 60 dB with no degradation in coherence. This strategy enables highly coherent channels with linewidths down to 10 Hz and power over 20 dBm. The overall electrical-to-optical efficiency reaches 19%, comparable to that of advanced semiconductor lasers. This method supports a silicon photonic communication link with an unprecedented data rate beyond 60 Tbit/s and reduces phase-related DSP consumption by 99.99999% compared to traditional III-V laser pump schemes. This work paves the way for realizing scalable, high-performance coherent integrated photonic systems, potentially benefiting numerous applications.

Amid the increasingly severe global climate change situation, green technology innovation has become an important means to promote carbon reduction and achieve the transition to a low-carbon economy. This study aims to systematically analyze the relationship between green technology innovation and carbon emission performance in the urban agglomeration of the middle reaches of the Yangtze River, exploring the degree of coupling and coordination between different cities. Utilizing data from 2011 to 2021, we employ methods such as the Coupling Coordination Degree Model and fixed effects model to achieve our objectives. Our findings reveal that both green technology innovation and carbon emission performance in this region are on an upward trend; however, the growth rate of green technology innovation showed a slowdown in 2021. Notably, there are disparities in the coupling coordination degree among cities, with economically developed areas exhibiting a faster growth rate. Moreover, green technology innovation significantly enhances carbon emission performance, and heterogeneity tests indicate that this impact is even more pronounced in cities with weaker environmental regulations. Despite regional differences, the overall trend remains positive. [ABSTRACT FROM AUTHOR]

To achieve the rapid grade classification of camellia seed oil, hyperspectral imaging technology was used to acquire hyperspectral images of three distinct grades of camellia seed oil. The spectral and image information collected by the hyperspectral imaging technology was preprocessed by different methods. The characteristic wavelength selection in this study included the continuous projections algorithm (SPA) and competitive adaptive reweighted sampling (CARS), and the gray-level co-occurrence matrix (GLCM) algorithm was used to extract the texture features of camellia seed oil at the characteristic wavelength. Combined with genetic algorithm (GA) and support vector machine algorithm (SVM), different grade classification models for camellia seed oil were developed using full wavelengths (GA-SVM), characteristic wavelengths (CARS-GA-SVM), and fusing spectral and image features (CARS-GLCM-GA-SVM). The results show that the CARS-GLCM-GA-SVM model, which combined spectral and image information, had the best classification effect, and the accuracy of the calibration set and prediction set of the CARS-GLCM-GA-SVM model were 98.30% and 96.61%, respectively. Compared with the CARS-GA-SVM model, the accuracy of the calibration set and prediction set were improved by 10.75% and 12.04%, respectively. Compared with the GA-SVM model, the accuracy of the calibration set and prediction set were improved by 18.28% and 18.15%, respectively. The research showed that hyperspectral imaging technology can rapidly classify camellia seed oil grades. [ABSTRACT FROM AUTHOR]

The widespread adoption of Internet of Things (IoT) applications has driven the demand for obtaining sensor data. Using unmanned aerial vehicles (UAVs) to collect sensor data is an effective means in scenarios with no ground communication facilities. In this paper, we innovatively consider an indeterministic data collection task in a UAV-assisted wide and sparse wireless sensor network, where the wireless sensor nodes (SNs) obtain effective data randomly, and the UAV has no pre-knowledge about which sensor has effective data. The UAV trajectories, SN serve scheduling and UAV-SN association are jointly optimized to maximize the amount of collected effective sensing data. We model the optimization problem and address the indeterministic effective indicator by introducing an effectiveness probability prediction model. The reformulated problem remains challenging to solve due to the number of constraints varying with the variable, i.e., the serve scheduling strategy. To tackle this issue, we propose a two-layer modified knapsack algorithm, within which a feasibility problem is resolved iteratively to find the optimal packing strategy. Numerical results demonstrate that the proposed scheme has remarkable advantages in the sum of effective data blocks, reducing the completion time for collecting the same ratio of effective data by nearly 30%. [ABSTRACT FROM AUTHOR]

Building on the recent demonstration of aqueous-dispersible hydrophobic pigments that retain their surface hydrophobicity even after drying, we demonstrate the synthesis of surface-modified Ti-Pure R-706 (denoted R706) titanium dioxide-based pigments, consisting of a thin (one to three monolayers) grafted polymethylhydrosiloxane (PMHS) coating, which (i) are hydrophobic in the dry state according to capillary rise and dynamic vapor sorption measurements and (ii) form stable aqueous dispersions at solid contents exceeding 75 wt % (43 vol %), without added dispersant, displaying similar rheology to R706 native oxide pigments at 70 wt % (37 vol %) consisting of an optimal amount of conventional polyanionic dispersant (0.3 wt % on pigment basis). The surface-modified pigments have been characterized via 29Si and 13C cross-polarization/magic angle spinning solid-state NMR spectroscopy; infrared spectroscopy; thermogravimetric and elemental analyses; and ζ potential measurements. On the basis of these data, the stability of the surface-modified PMHS-R706 aqueous dispersions is attributed to steric effects, as a result of grafted PMHS strands on the R706 surface, and depends on the chaotropic nature of the base used during PMHS condensation to the pigment/polysiloxane interface. The lack of water wettability of the surface-modified oxide particles in their dry state translates to improved water-barrier properties in coatings produced with these surface-modified pigment particles. The synthetic approach appears general as demonstrated by its application to various inorganic-oxide pigment particles.