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Humans can naturally and effectively find salient regions in complex scenes. Motivated by this observation, attention mechanisms were introduced into computer vision with the aim of imitating this aspect of the human visual system. Such an attention mechanism can be regarded as a dynamic weight adjustment process based on features of the input image. Attention mechanisms have achieved great success in many visual tasks, including image classification, object detection, semantic segmentation, video understanding, image generation, 3D vision, multimodal tasks, and self-supervised learning. In this survey, we provide a comprehensive review of various attention mechanisms in computer vision and categorize them according to approach, such as channel attention, spatial attention, temporal attention, and branch attention; a related repository https://github.com/MenghaoGuo/Awesome-Vision-Attentions is dedicated to collecting related work. We also suggest future directions for attention mechanism research.

In this work, the effects of lignin on the thermal stability of bagasse cellulose nanofibrils (CNFs) were investigated. The CNFs were prepared with different lignin content bagasse pulp using ultrafine grinding combined with high-pressure homogenization. Scanning electron microscopy, Fourier transform infrared spectroscopy, X-ray diffraction and thermogravimetric analysis were used to study the influence mechanisms of lignin content on the thermal stability of the CNFs. The thermal stability of cellulose was tested by thermogravimetric analyzer at different heating rates, and the activation energy of bagasse cellulose nanofibrils was calculated by Flynn-Wall-Ozawa method. The results showed that the average width of CNFs prepared by the mechanical method was approximately 20 nm. The higher the lignin contents in the CNFs, the lower the crystallinity and the better the thermal stability. The thermal decomposition activation energy of CNF fluctuates with the change of conversion rate. Under the same conversion rate, the higher the lignin contents in the CNFs, the larger the activation energy value. The average activation energies of NO-LCNF, L-LCNF, ML-LCNF, MH-LCNF, and H-LCNF were 208.14, 254.49, 412.95, 530.54 and 652.10 kJ/mol, respectively, during the conversion rate of 20% to 90%. The research results provide a theoretical basis for the pyrolysis mechanism and high value utilization of CNFs and have a profound impact on promoting the application and development of CNFs in emerging nanocomposites. Graphic abstract

As a ubiquitous solar-thermal energy conversion process, solar-driven evaporation has attracted tremendous research attention owing to its high conversion efficiency of solar energy and transformative industrial potential. In recent years, solar-driven interfacial evaporation by localization of solar-thermal energy conversion to the air/liquid interface has been proposed as a promising alternative to conventional bulk heating-based evaporation, potentially reducing thermal losses and improving energy conversion efficiency. In this Review, we discuss the development of the key components for achieving high-performance evaporation, including solar absorbers, evaporation structures, thermal insulators and thermal concentrators, and discuss how they improve the performance of the solar-driven interfacial evaporation system. We describe the possibilities for applying this efficient solar-driven interfacial evaporation process for energy conversion applications. The exciting opportunities and challenges in both fundamental research and practical implementation of the solar-driven interfacial evaporation process are also discussed. The thermal properties of solar energy can be exploited for many applications, including evaporation. Tao et al. review recent developments in the field of solar-driven interfacial evaporation, which have enabled higher-performance structures by localizing energy conversion to the air/liquid interface.

Noncontact human-machine interaction provides a hygienic and intelligent approach for the communication between human and robots. Current noncontact human-machine interactions are generally limited by the interaction distance or conditions, such as in the dark. Here we explore the utilization of hand as an infrared light source for noncontact human-machine interaction. Metallic gratings are used as the human-machine interface to respond to infrared radiation from hand and the generated signals are visualized as different infrared structural colors. We demonstrate the applications of the infrared structural color-based human-machine interaction for user-interactive touchless display and real-time control of a robot vehicle. The interaction is flexible to the hand-interface distance ranging from a few centimeters to tens of centimeters and can be used in low lighting condition or in the dark. The findings in this work provide an alternative and complementary approach to traditional noncontact human-machine interactions, which may further broaden the potential applications of human-machine interaction. The IR radiation from human hand can selectively interact with grating patterns in the generation of distinct IR structural colors, which can be used for human-machine interaction with flexible interaction distance in low or no light conditions.

A straightforward global layout of carbon capture, utilization and storage (CCUS) is imperative for limiting global warming well below 2 °C. Here, we propose a cost-effective strategy for matching carbon sources and sinks on a global scale. Results show 3,093 carbon clusters and 432 sinks in 85 countries and regions are selected to achieve 92 GtCO2 mitigation by CCUS, 64% of which will be sequestered into sedimentary basins for aquifer storage and 36% will be used for CO2-EOR (enhanced oil recovery). Of the identified source–sink matching, 80% are distributed within 300 km and are mainly located in China, the United States, the European Union, Russia and India. The total cost is ~0.12% of global cumulative gross domestic product. Of countries with CO2-EOR, 75% will turn into profitable at the oil price over US$100 per barrel. These findings indicate our proposed layout is economically feasible. However, its implementation requires global collaboration on financial and technological transfer.Carbon capture, utilization and storage (CCUS) will be required to meet climate targets. An economically feasible global CCUS layout can be achieved by capturing the carbon sources in 85 regions and mitigating with 59 GtCO2 sequestration and aquifer storage and 33 GtCO2-enhanced oil recovery.

Microbial biodegradation is one of the acceptable technologies to remediate and control the pollution by polycyclic aromatic hydrocarbon (PAH). Several bacteria, fungi, and cyanobacteria strains have been isolated and used for bioremediation purpose. This review paper is intended to provide key information on the various steps and actors involved in the bacterial and fungal aerobic and anaerobic degradation of pyrene, a high molecular weight PAH, including catabolic genes and enzymes, in order to expand our understanding on pyrene degradation. The aerobic degradation pathway by Mycobacterium vanbaalenii PRY-1 and Mycobactetrium sp. KMS and the anaerobic one, by the facultative bacteria anaerobe Pseudomonas sp. JP1 and Klebsiella sp. LZ6 are reviewed and presented, to describe the complete and integrated degradation mechanism pathway of pyrene. The different microbial strains with the ability to degrade pyrene are listed, and the degradation of pyrene by consortium is also discussed. The future studies on the anaerobic degradation of pyrene would be a great initiative to understand and address the degradation mechanism pathway, since, although some strains are identified to degrade pyrene in reduced or total absence of oxygen, the degradation pathway of more than 90% remains unclear and incomplete. Additionally, the present review recommends the use of the combination of various strains of anaerobic fungi and a fungi consortium and anaerobic bacteria to achieve maximum efficiency of the pyrene biodegradation mechanism.

Gastric cancer (GC) is a common malignant tumor worldwide, characterized by complex biological processes. The distribution of various cell types and gene expression profiles in the GC microenvironment remains unclear. This study uses single-cell RNA sequencing to explore gene expression patterns and identify differentially expressed genes in GC samples, offering new insights into cellular diversity and potential molecular mechanisms. We conducted temporal and clustering analyses with single-cell sequencing, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses to clarify their functions. Using machine learning, we identified relevant genes to create highly accurate prediction models. Additionally, ssGSEA analysis provided detailed insights into the immunosuppressive tumor microenvironment, revealing complex gene expression interactions and diverse immune infiltrates in cancer. Correlation analysis highlighted TIMP1 as having significant prognostic value across different immune cell subtypes. Single-cell RNA sequencing revealed the cellular landscape and gene expression profiles of the GC microenvironment, offering crucial data on how cell heterogeneity is regulated in relation to the tumor microenvironment. Moreover, new insights into the expression levels of AGT, INHBA, and TIMP1 showed distinct sex-biased gene functions within the tumor microenvironment. These findings enhance our understanding of the molecular mechanisms associated with gastric cancer development and may lay the groundwork for identifying novel therapeutic targets and diagnostic strategies.

Currently, solar-thermal energy storage within phase-change materials relies on adding high thermal-conductivity fillers to improve the thermal-diffusion-based charging rate, which often leads to limited enhancement of charging speed and sacrificed energy storage capacity. Here we report the exploration of a magnetically enhanced photon-transport-based charging approach, which enables the dynamic tuning of the distribution of optical absorbers dispersed within phase-change materials, to simultaneously achieve fast charging rates, large phase-change enthalpy, and high solar-thermal energy conversion efficiency. Compared with conventional thermal charging, the optical charging strategy improves the charging rate by more than 270% and triples the amount of overall stored thermal energy. This superior performance results from the distinct step-by-step photon-transport charging mechanism and the increased latent heat storage through magnetic manipulation of the dynamic distribution of optical absorbers. Solar-thermal energy storage based on phase-change materials suffers from slow thermal-diffusion-based charging. Here the authors alleviate this issue by introducing optical absorbers and controlling their distribution to accelerate charging process and thus improve solar-thermal energy conversion.

The realm of computer vision has witnessed a paradigm shift with the advent of foundational models, mirroring the transformative influence of large language models in the domain of natural language processing. This paper delves into the exploration of open-world segmentation, presenting a novel approach called Image Prompt Segmentation (IPSeg) that harnesses the power of vision foundational models. IPSeg lies the principle of a training-free paradigm, which capitalizes on image prompt techniques. Specifically, IPSeg utilizes a single image containing a subjective visual concept as a flexible prompt to query vision foundation models like DINOv2 and Stable Diffusion. Our approach extracts robust features for the prompt image and input image, then matches the input representations to the prompt representations via a novel feature interaction module to generate point prompts highlighting target objects in the input image. The generated point prompts are further utilized to guide the Segment Anything Model to segment the target object in the input image. The proposed method stands out by eliminating the need for exhaustive training sessions, thereby offering a more efficient and scalable solution. Experiments on COCO, PASCAL VOC, and other datasets demonstrate IPSeg’s efficacy for flexible open-world segmentation using intuitive image prompts. This work pioneers tapping foundation models for open-world understanding through visual concepts conveyed in images.

The absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties are important in drug discovery as they define efficacy and safety. In this work, we applied an ensemble of features, including fingerprints and descriptors, and a tree-based machine learning model, extreme gradient boosting, for accurate ADMET prediction. Our model performs well in the Therapeutics Data Commons ADMET benchmark group. For 22 tasks, our model is ranked first in 18 tasks and top 3 in 21 tasks. The trained machine learning models are integrated in ADMETboost, a web server that is publicly available at https://ai-druglab.smu.edu/admet .