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Many exciting terahertz imaging applications, such as non-destructive evaluation, biomedical diagnosis, and security screening, have been historically limited in practical usage due to the raster-scanning requirement of imaging systems, which impose very low imaging speeds. However, recent advancements in terahertz imaging systems have greatly increased the imaging throughput and brought the promising potential of terahertz radiation from research laboratories closer to real-world applications. Here, we review the development of terahertz imaging technologies from both hardware and computational imaging perspectives. We introduce and compare different types of hardware enabling frequency-domain and time-domain imaging using various thermal, photon, and field image sensor arrays. We discuss how different imaging hardware and computational imaging algorithms provide opportunities for capturing time-of-flight, spectroscopic, phase, and intensity image data at high throughputs. Furthermore, the new prospects and challenges for the development of future high-throughput terahertz imaging systems are briefly introduced.

Nonlinear encoding of optical information can be achieved using various forms of data representation. Here, we analyze the performances of different nonlinear information encoding strategies that can be employed in diffractive optical processors based on linear materials and shed light on their utility and performance gaps compared to the state-of-the-art digital deep neural networks. For a comprehensive evaluation, we used different datasets to compare the statistical inference performance of simpler-to-implement nonlinear encoding strategies that involve, e.g., phase encoding, against data repetition-based nonlinear encoding strategies. We show that data repetition within a diffractive volume (e.g., through an optical cavity or cascaded introduction of the input data) causes the loss of the universal linear transformation capability of a diffractive optical processor. Therefore, data repetition-based diffractive blocks cannot provide optical analogs to fully connected or convolutional layers commonly employed in digital neural networks. However, they can still be effectively trained for specific inference tasks and achieve enhanced accuracy, benefiting from the nonlinear encoding of the input information. Our results also reveal that phase encoding of input information without data repetition provides a simpler nonlinear encoding strategy with comparable statistical inference accuracy to data repetition-based diffractive processors. Our analyses and conclusions would be of broad interest to explore the push-pull relationship between linear material-based diffractive optical systems and nonlinear encoding strategies in visual information processors.

The electrochemical conversion of CO 2 to methane provides a means to store intermittent renewable electricity in the form of a carbon-neutral hydrocarbon fuel that benefits from an established global distribution network. The stability and selectivity of reported approaches reside below technoeconomic-related requirements. Membrane electrode assembly-based reactors offer a known path to stability; however, highly alkaline conditions on the cathode favour C-C coupling and multi-carbon products. In computational studies herein, we find that copper in a low coordination number favours methane even under highly alkaline conditions. Experimentally, we develop a carbon nanoparticle moderator strategy that confines a copper-complex catalyst when employed in a membrane electrode assembly. In-situ XAS measurements confirm that increased carbon nanoparticle loadings can reduce the metallic copper coordination number. At a copper coordination number of 4.2 we demonstrate a CO 2 -to-methane selectivity of 62%, a methane partial current density of 136 mA cm −2 , and > 110 hours of stable operation. Electrochemical conversion of carbon dioxide to methane can store intermittent renewable electricity in a staple of global energy. Here, the authors develop a moderator strategy to maintain the catalyst in a low coordination state, thereby enabling stable and selective electrochemical methanation.

East Asia is bounded by the Indian plate to the southwest and the Pacific and Philippine plates to the east, and has undergone complex tectonic evolution since ~55 Ma. In this study, we collect and process three sources of GPS datasets, including GPS observations, GPS positioning time series, and published GPS velocities, to derive unified velocity and strain rate fields for East Asia. We observed southward movement and arc-parallel extension along the Ryukyu Arc and propose that the maximum principal stress axis (striking NEE) in North China could be mainly induced by westward subduction of the Pacific plate and the southward movement of the Ryukyu Arc. The large EW-trending sinistral shear zone that bounds North China has been created by eastward movement of South China to the south and westward subduction of the Pacific plate to the north. GPS velocity profiles and strain rates also demonstrate that crustal deformation in mainland China is controlled by northeastward collision of the Indian plate into Eurasia and westward subduction of the Pacific and Philippine Sea plates beneath Eurasia. In particular, the India-Eurasia continental collision has the most extensive impact, which can reach as far as the southern Lake Baikal. The viscous behavior of the subducting Pacific slab also drives interseismic deformation of North China. The crustal deformation caused by Philippine oceanic subduction is small and is limited to the region between the southeast coast of mainland China and Taiwan island. However, the principal compressional strain around eastern Taiwan is the largest in the region.

Electrochemical reduction of CO 2 to multi-carbon products (C 2+ ), when powered using renewable electricity, offers a route to valuable chemicals and fuels. In conventional neutral-media CO 2 -to-C 2+ devices, as much as 70% of input CO 2 crosses the cell and mixes with oxygen produced at the anode. Recovering CO 2 from this stream adds a significant energy penalty. Here we demonstrate that using a liquid-to-liquid anodic process enables the recovery of crossed-over CO 2 via facile gas-liquid separation without additional energy input: the anode tail gas is directly fed into the cathodic input, along with fresh CO 2 feedstock. We report a system exhibiting a low full-cell voltage of 1.9 V and total carbon efficiency of 48%, enabling 262 GJ/ton ethylene, a 46% reduction in energy intensity compared to state-of-art single-stage CO 2 -to-C 2+ devices. The strategy is compatible with today’s highest-efficiency electrolyzers and CO 2 catalysts that function optimally in neutral and alkaline electrolytes. In the electrified conversion of CO2 to multicarbon products, CO2 crossover to the O2-rich anodic stream adds a further, energy-intensive, chemical separation step. Here, the authors demonstrate a strategy that eliminates the separation requirement.

Deepwater oil exhibits great difficulty and risk in extraction under complex marine environments and strong ocean currents, and the operation and maintenance of its underwater control system rely heavily on remote operation. Traditional control methods make it hard to satisfy the demands of extraction. Therefore, the study first proposes simulating the marine working environment using virtual technology and model the Remote Operated Vehicle (ROV) to optimize power allocation. Secondly, the robot grasping task is achieved by designing binocular vision stereo image matching and improving the Proximal Policy Optimization grasping algorithm to enhance its stability and operational success rate under turbulent disturbances. Finally, an underwater production simulation system is built to provide a virtualization platform for oil and gas development operations. The results show that ROV can effectively achieve propeller power distribution, and the amplitude error under different operating conditions is reduced by an average of about 25% compared to the traditional Saab Seaeye model, with a maximum of no more than 5%. The simulation effect is significant. And the robot grasping system designed by the research institute can autonomously complete tasks. After training, the positioning error is reduced by 41.6% and 74.7% compared to the Mask Region based Convolutional Neural Network (Mask R-CNN) algorithm and You Only Look Once algorithm, respectively, reaching a final height of 0.11 m. The grasping success rate is still better than the comparative algorithm by more than 10% and more than 90% in strong flow environments, and it takes less time to improve overall performance significantly. The research method can provide a guarantee for the safety of deepwater operations in oil and gas fields, and reduce operation and maintenance costs and mining difficulties.

Nitrogen-doped graphene-supported single atoms convert CO 2 to CO, but fail to provide further hydrogenation to methane – a finding attributable to the weak adsorption of CO intermediates. To regulate the adsorption energy, here we investigate the metal-supported single atoms to enable CO 2 hydrogenation. We find a copper-supported iron-single-atom catalyst producing a high-rate methane. Density functional theory calculations and in-situ Raman spectroscopy show that the iron atoms attract surrounding intermediates and carry out hydrogenation to generate methane. The catalyst is realized by assembling iron phthalocyanine on the copper surface, followed by in-situ formation of single iron atoms during electrocatalysis, identified using operando X-ray absorption spectroscopy. The copper-supported iron-single-atom catalyst exhibits a CO 2 -to-methane Faradaic efficiency of 64% and a partial current density of 128 mA cm −2 , while the nitrogen-doped graphene-supported one produces only CO. The activity is 32 times higher than a pristine copper under the same conditions of electrolyte and bias. Converting CO2 and H2O into value-added chemical feedstocks and fuels offers a carbon neutral approach to tackling global energy and climate concerns. Here the authors report a metal supported single-atom catalytic site enabling the electrocatalytic reduction of CO2 to methane.

The consumption forecasting of oil and coal can help governments optimize and adjust energy strategies to ensure energy security in China. However, such forecasting is extremely challenging because it is influenced by many complex and uncertain factors. To fill this gap, we propose a hybrid deep learning approach for consumption forecasting of oil and coal in China. It consists of three parts, i.e., feature engineering, model building, and model integration. First, feature engineering is to distinguish the different correlations between targeted indicators and various features. Second, model building is to build five typical deep learning models with different characteristics to forecast targeted indicators. Third, model integration is to ensemble the built five models with a tailored, self-adaptive weighting strategy. As such, our approach enjoys all the merits of the five deep learning models (they have different learning structures and temporal constraints to diversify them for ensembling), making it able to comprehensively capture all the characteristics of different indicators to achieve accurate forecasting. To evaluate the proposed approach, we collected the real 880 pieces of data with 39 factors regarding the energy consumption of China ranging from 1999 to 2021. By conducting extensive experiments on the collected datasets, we have identified the optimal features for four targeted indicators (i.e., import of oil, production of oil, import of coal, and production of coal), respectively. Besides, we have demonstrated that our approach is significantly more accurate than the state-of-the-art forecasting competitors.

Terahertz waves offer advantages for nondestructive detection of hidden objects/defects in materials, as they can penetrate most optically-opaque materials. However, existing terahertz inspection systems face throughput and accuracy restrictions due to their limited imaging speed and resolution. Furthermore, machine-vision-based systems using large-pixel-count imaging encounter bottlenecks due to their data storage, transmission and processing requirements. Here, we report a diffractive sensor that rapidly detects hidden defects/objects within a 3D sample using a single-pixel terahertz detector, eliminating sample scanning or image formation/processing. Leveraging deep-learning-optimized diffractive layers, this diffractive sensor can all-optically probe the 3D structural information of samples by outputting a spectrum, directly indicating the presence/absence of hidden structures or defects. We experimentally validated this framework using a single-pixel terahertz time-domain spectroscopy set-up and 3D-printed diffractive layers, successfully detecting unknown hidden defects inside silicon samples. This technique is valuable for applications including security screening, biomedical sensing and industrial quality control. Researchers showcase a deep learning-designed diffractive terahertz sensor that rapidly detects hidden defects within 3D samples based on the output spectrum measured by a single-pixel detector, eliminating sample scanning or image formation/processing.

Micro-expressions, which are spontaneous and difficult to suppress, reveal a person’s true emotions. They are characterized by short duration and low intensity, making the task of micro-expression recognition challenging in the field of emotion computing. In recent years, deep learning-based feature extraction and fusion techniques have been widely used for micro-expression recognition, particularly methods based on Vision Transformer that have gained popularity. However, the Vision Transformer-based architecture used in micro-expression recognition involves a significant amount of invalid computation. Additionally, in the traditional two-stream architecture, although separate streams are combined through late fusion, only the output features from the deepest level of the network are utilized for classification, thus limiting the network’s ability to capture subtle details due to the lack of fine-grained information. To address these issues, we propose a new two-level spatio-temporal feature fused with a two-stream architecture. This architecture includes a spatial encoder (modified ResNet) for learning texture features of the face, a temporal encoder (Swin Transformer) for learning facial muscle motor features, a feature fusion algorithm for integrating multi-level spatio-temporal features, a classification head, and a weighted average operator for temporal aggregation. The two-stream architecture has the advantage of extracting richer features compared to the single-stream architecture, leading to improved performance. The shifted window scheme of Swin Transformer restricts self-attention computation to non-overlapping local windows and allows cross-window connections, significantly improving the performance and reducing the computation compared to Vision Transformer. Moreover, the modified ResNet is computationally less intensive. Our proposed feature fusion algorithm leverages the similarity in output feature shapes at each stage of the two streams, enabling the effective fusion of multi-level spatio-temporal features. This algorithm results in an improvement of approximately 4% in both the F1 score and the UAR. Comprehensive evaluations conducted on three widely used spontaneous micro-expression datasets (SMIC-HS, CASME II, and SAMM) consistently demonstrate the superiority of our approach over comparative methods. Notably, our approach achieves a UAR exceeding 0.905 on CASME II, making it one of the few frameworks in the published micro-expression recognition literature to achieve such high performance.