The release of the generative pre-trained transformer (GPT) series has brought artificial general intelligence (AGI) to the forefront of the artificial intelligence (AI) field once again. However, the questions of how to define and evaluate AGI remain unclear. This perspective article proposes that the evaluation of AGI should be rooted in dynamic embodied physical and social interactions (DEPSI). More specifically, we propose five critical characteristics to be considered as AGI benchmarks and suggest the Tong test as an AGI evaluation system. The Tong test describes a value- and ability-oriented testing system that delineates five levels of AGI milestones through a virtual environment with DEPSI, allowing for infinite task generation. We contrast the Tong test with classical AI testing systems in terms of various aspects and propose a systematic evaluation system to promote standardized, quantitative, and objective benchmarks and evaluation of AGI.
With the development of edge devices and cloud computing, the question of how to accomplish machine learning and optimization tasks in a privacy-preserving and secure way has attracted increased attention over the past decade. As a privacy-preserving distributed machine learning method, federated learning (FL) has become popular in the last few years. However, the data privacy issue also occurs when solving optimization problems, which has received little attention so far. This survey paper is concerned with privacy-preserving optimization, with a focus on privacy-preserving data-driven evolutionary optimization. It aims to provide a roadmap from secure privacy-preserving learning to secure privacy-preserving optimization by summarizing security mechanisms and privacy-preserving approaches that can be employed in machine learning and optimization. We provide a formal definition of security and privacy in learning, followed by a comprehensive review of FL schemes and cryptographic privacy-preserving techniques. Then, we present ideas on the emerging area of privacy-preserving optimization, ranging from privacy-preserving distributed optimization to privacy-preserving evolutionary optimization and privacy-preserving Bayesian optimization (BO). We further provide a thorough security analysis of BO and evolutionary optimization methods from the perspective of inferring attacks and active attacks. On the basis of the above, an in-depth discussion is given to analyze what FL and distributed optimization strategies can be used for the design of federated optimization and what additional requirements are needed for achieving these strategies. Finally, we conclude the survey by outlining open questions and remaining challenges in federated data-driven optimization. We hope this survey can provide insights into the relationship between FL and federated optimization and will promote research interest in secure federated optimization.
Tax risk behavior causes serious loss of fiscal revenue, damages the country’s public infrastructure, and disturbs the market economic order of fair competition. In recent years, tax risk detection, driven by information technology such as data mining and artificial intelligence, has received extensive attention. To promote the high-quality development of tax risk detection methods, this paper provides the first comprehensive overview and summary of existing tax risk detection methods worldwide. More specifically, it first discusses the causes and negative impacts of tax risk behaviors, along with the development of tax risk detection. It then focuses on data-mining-based tax risk detection methods utilized around the world. Based on the different principles employed by the algorithms, existing risk detection methods can be divided into two categories: relationship-based and non-relationship-based. A total of 14 risk detection methods are identified, and each method is thoroughly explored and analyzed. Finally, four major technical bottlenecks of current data-driven tax risk detection methods are analyzed and discussed, including the difficulty of integrating and using fiscal and tax fragmented knowledge, unexplainable risk detection results, the high cost of risk detection algorithms, and the reliance of existing algorithms on labeled information. After investigating these issues, it is concluded that knowledge-guided and data-driven big data knowledge engineering will be the development trend in the field of tax risk in the future; that is, the gradual transition of tax risk detection from informatization to intelligence is the future development direction.
Encouraging and astonishing developments have recently been achieved in image-based diagnostic technology. Modern medical care and imaging technology are becoming increasingly inseparable. However, the current diagnosis pattern of signal to image to knowledge inevitably leads to information distortion and noise introduction in the procedure of image reconstruction (from signal to image). Artificial intelligence (AI) technologies that can mine knowledge from vast amounts of data offer opportunities to disrupt established workflows. In this prospective study, for the first time, we develop an AI-based signal-to-knowledge diagnostic scheme for lung nodule classification directly from the computed tomography (CT) raw data (the signal). We find that the raw data achieves almost comparable performance with CT, indicating that it is possible to diagnose diseases without reconstructing images. Moreover, the incorporation of raw data through three common convolutional network structures greatly improves the performance of the CT models in all cohorts (with a gain ranging from 0.01 to 0.12), demonstrating that raw data contains diagnostic information that CT does not possess. Our results break new ground and demonstrate the potential for direct signal-to-knowledge domain analysis.
Anticipating others’ actions is innate and essential in order for humans to navigate and interact well with others in dense crowds. This ability is urgently required for unmanned systems such as service robots and self-driving cars. However, existing solutions struggle to predict pedestrian anticipation accurately, because the influence of group-related social behaviors has not been well considered. While group relationships and group interactions are ubiquitous and significantly influence pedestrian anticipation, their influence is diverse and subtle, making it difficult to explicitly quantify. Here, we propose the group interaction field (GIF), a novel group-aware representation that quantifies pedestrian anticipation into a probability field of pedestrians’ future locations and attention orientations. An end-to-end neural network, GIFNet, is tailored to estimate the GIF from explicit multidimensional observations. GIFNet quantifies the influence of group behaviors by formulating a group interaction graph with propagation and graph attention that is adaptive to the group size and dynamic interaction states. The experimental results show that the GIF effectively represents the change in pedestrians’ anticipation under the prominent impact of group behaviors and accurately predicts pedestrians’ future states. Moreover, the GIF contributes to explaining various predictions of pedestrians’ behavior in different social states. The proposed GIF will eventually be able to allow unmanned systems to work in a human-like manner and comply with social norms, thereby promoting harmonious human-machine relationships.
Acute aortic dissection is one of the most life-threatening cardiovascular diseases, with a high mortality rate. Its prevalence ranges from 0.2% to 0.8% in humans, resulting in a significant number of deaths due to being missed in manual examinations. More importantly, the aortic diameter—a critical indicator for surgical selection—significantly influences the outcomes of surgeries post-diagnosis. Therefore, it is an urgent yet challenging mission to develop an automatic aortic dissection diagnostic system that can recognize and classify the aortic dissection type and measure the aortic diameter. This paper offers a dual-functional deep learning system called aortic dissections diagnosis-aiding system (DDAsys) that enables both accurate classification of aortic dissection and precise diameter measurement of the aorta. To this end, we created a dataset containing 61 190 computed tomography angiography (CTA) images from 279 patients from the Division of Cardiovascular Surgery at Tongji Hospital, Wuhan, China. The dataset provides a slice-level summary of difficult-to-identify features, which helps to improve the accuracy of both recognition and classification. Our system achieves a recognition F1 score of 0.984, an average classification F1 score of 0.935, and the respective measurement precisions for ascending and descending aortic diameters are 0.994 mm and 0.767 mm root mean square error (RMSE). The high consistency (88.6%) between the recommended surgical treatments and the actual corresponding surgeries verifies the capability of our system to aid clinicians in developing a more prompt, precise, and consistent treatment strategy.
In recent years, significant progress has been made in both three-dimensional (3D) printing technologies and the exploration of silk as an ink to produce biocompatible constructs. Combined with the unlimited design potential of 3D printing, silk can be processed into a broad range of functional materials and devices for various biomedical applications. The ability of silk to be processed into various materials, including solutions, hydrogels, particles, microspheres, and fibers, makes it an excellent candidate for adaptation to different 3D printing techniques. This review presents a didactic overview of the 3D printing of silk-based materials, major categories of printing techniques, and their prototyping mechanisms and structural features. In addition, we provide a roadmap for researchers aiming to incorporate silk printing into their own work by summarizing promising strategies from both technical and material aspects, to relate state-of-the-art silk-based material processing with fast-developing 3D printing technologies. Thus, our focus is on elucidating the techniques and strategies that advance the development of precise assembly strategies for silk-based materials. Precise printing (including high printing resolution, complex structure realization, and printing fidelity) is a prerequisite for the digital design capability of 3D printing technology and would definitely broaden the application era of silk, such as complex biomimetic tissue structures, vasculatures, and transdermal microneedles.
Globally, vegetation has been changing dramatically. The vegetation-water dynamic is key to understanding ecosystem structure and functioning in water-limited ecosystems. Continual satellite monitoring has detected global vegetation greening. However, a vegetation greenness increase does not mean that ecosystem functions increase. The intricate interplays resulting from the relationships between vegetation and precipitation must be more adequately comprehended. In this study, satellite data, for example, leaf area index (LAI), net primary production (NPP), and rainfall use efficiency (RUE), were used to quantify vegetation dynamics and their relationship with rainfall in different reaches of the Yellow River Basin (YRB). A sequential regression method was used to detect trends of NPP sensitivity to rainfall. The results showed that 34.53% of the YRB exhibited a significant greening trend since 2000. Among them, 20.54%, 53.37%, and 16.73% of upper, middle, and lower reach areas showed a significant positive trend, respectively. NPP showed a similar trend to LAI in the YRB upper, middle, and lower reaches. A notable difference was noted in the distributions and trends of RUE across the upper, middle, and lower reaches. Moreover, there were significant trends in vegetation-rainfall sensitivity in 16.86% of the YRB’s middle reaches—14.08% showed negative trends and 2.78% positive trends. A total of 8.41% of the YRB exhibited a marked increase in LAI, NPP, and RUE. Subsequently, strategic locations reliant on the correlation between vegetation and rainfall were identified and designated for restoration planning purposes to propose future ecological restoration efforts. Our analysis indicates that the middle reach of the YRB exhibited the most significant variation in vegetation greenness and productivity. The present study underscores the significance of examining the correlation between vegetation and rainfall within the context of the high-quality development strategy of the YRB. The outcomes of our analysis and the proposed ecological restoration framework can provide decision-makers with valuable insights for executing rational basin pattern optimization and sustainable management.
The sustainable recovery and utilization of sludge bioenergy within a circular economy context has drawn increasing attention, but there is currently a shortage of reliable technology. This study presents an innovative biotechnology based on free nitrous acid (FNA) to realize sustainable organics recovery from waste activated sludge (WAS) in-situ, driving efficient nitrogen removal from ammonia rich mature landfill leachate by integrating partial nitrification, fermentation, and denitrification process (PN/DN-F/DN). First, ammonia ((1708.5 ± 142.9) mg·L−1) in mature landfill leachate is oxidized to nitrite in the aerobic stage of a partial nitrification coupling denitrification (PN/DN) sequencing batch reactor (SBR), with nitrite accumulation ratio of 95.4% ± 2.5%. Then, intermediate effluent (NO2−-N = (1196.9 ± 184. 2) mg·L−1) of the PN/DN-SBR, along with concentrated WAS (volatile solids (VSs) = (15 119.8 ± 2 484.2) mg·L−1), is fed into an anoxic reactor for fermentation coupling denitrification process (F/DN). FNA, the protonated form of nitrite, degrades organics in the WAS to the soluble fraction by the biocidal effect, and the released organics are utilized by denitrifiers to drive NOx− reduction. An ultra-fast sludge reduction rate of 4.89 kg·m−3·d−1 and nitrogen removal rate of 0.46 kg·m−3·d−1 were realized in the process. Finally, F/DN-SBR effluent containing organics is refluxed to PN/DN-SBR for secondary denitrification in the post anoxic stage. After 175 d operation, an average of 19 350.6 mg chemical oxygen demand organics were recovered per operational cycle, with 95.2% nitrogen removal and 53.4% sludge reduction. PN/DN-F/DN is of great significance for promoting a paradigm transformation from energy consumption to energy neutral, specifically, the total benefit in equivalent terms of energy was 291.8 kW·h·t−1 total solid.
The Tibetan Plateau (TP) is the headwater of the Yangtze, Yellow, and the transboundary Yarlung Zangbo, Lancang, and Nujiang Rivers, providing essential and pristine freshwater to around 1.6 billion people in Southeast and South Asia. However, the temperature rise TP has experienced is almost three times that of the global warming rate. The rising temperature has resulted in glacier retreat, snow cover reduction, permafrost layer thawing, and so forth. Here we show, based on the longest observed streamflow data available for the region so far, that changing climatic conditions in the TP already had significant impacts on the streamflow in the headwater basins in the area. Our analysis indicated that the annual average temperature in the headwater basins of these five major rivers has been rising on a trend averaging 0.38 °C·decade−1 since 1998, almost triple the rate before 1998, and the change of streamflow has been predominantly impacted by precipitation in these headwater basins. As a result, streamflow in the Yangtze, Yarlung Zangbo, Lancang, and Nujiang River headwater areas is on a decreasing trend with a reduction of flow ranging from 3.0 ×109-5.9 ×109 m3·decade−1 (−9.12% to −16.89% per decade) since 1998. The increased precipitation in the Tangnahai (TNH) and Lanzhou (LZ) Basins contributed to the increase of their streamflows at 8.04% and 14.29% per decade, respectively. Although the increased streamflow in the headwater basins of the Yellow River may ease some of the water resources concerns, the decreasing trend of streamflow in the headwater areas of the southeastern TP region since 1998 could lead to a water crisis in transboundary river basins for billions of people in Southeast and South Asia.
The patterns of material accumulation in buildings and infrastructure accompanied by rapid urbanization offer an important, yet hitherto largely missing stock perspective for facilitating urban system engineering and informing urban resources, waste, and climate strategies. However, our existing knowledge on the patterns of built environment stocks across and particularly within cities is limited, largely owing to the lack of sufficient high spatial resolution data. This study leveraged multi-source big geodata, machine learning, and bottom-up stock accounting to characterize the built environment stocks of 50 cities in China at 500 m fine-grained levels. The per capita built environment stock of many cities (261 tonnes per capita on average) is close to that in western cities, despite considerable disparities across cities owing to their varying socioeconomic, geomorphology, and urban form characteristics. This is mainly owing to the construction boom and the building and infrastructure-driven economy of China in the past decades. China’s urban expansion tends to be more “vertical” (with high-rise buildings) than “horizontal” (with expanded road networks). It trades skylines for space, and reflects a concentration-dispersion-concentration pathway for spatialized built environment stocks development within cities in China. These results shed light on future urbanization in developing cities, inform spatial planning, and support circular and low-carbon transitions in cities.
Rockburst disasters occur frequently during deep underground excavation, yet traditional concepts and methods can hardly meet the requirements for support under high geo-stress conditions. Consequently, rockburst control remains challenging in the engineering field. In this study, the mechanism of excavation-induced rockburst was briefly described, and it was proposed to apply the excavation compensation method (ECM) to rockburst control. Moreover, a field test was carried out on the Qinling Water Conveyance Tunnel. The following beneficial findings were obtained: Excavation leads to changes in the engineering stress state of surrounding rock and results in the generation of excess energy ΔE, which is the fundamental cause of rockburst. The ECM, which aims to offset the deep excavation effect and lower the risk of rockburst, is an active support strategy based on high pre-stress compensation. The new negative Poisson’s ratio (NPR) bolt developed has the mechanical characteristics of high strength, high toughness, and impact resistance, serving as the material basis for the ECM. The field test results reveal that the ECM and the NPR bolt succeed in controlling rockburst disasters effectively. The research results are expected to provide guidance for rockburst support in deep underground projects such as Sichuan-Xizang Railway.
The development of effective antifreeze peptides to control ice growth has attracted a significant amount of attention yet still remains a great challenge. Here, we propose a novel design method based on in-depth investigation of repetitive motifs in various ice-binding proteins (IBPs) with evolution analysis. In this way, several peptides with notable antifreeze activity were developed. In particular, a designed antifreeze peptide named AVD exhibits ideal ice recrystallization inhibition (IRI), solubility, and biocompatibility, making it suitable for use as a cryoprotective agent (CPA). A mutation analysis and molecular dynamics (MD) simulations indicated that the Thr6 and Asn8 residues of the AVD peptide are fundamental to its ice-binding capacity, while the Ser18 residue can synergistically enhance their interaction with ice, revealing the antifreeze mechanism of AVD. Furthermore, to evaluate the cryoprotection potential of AVD, the peptide was successfully employed for the cryopreservation of various cells, which demonstrated significant post-freezing cell recovery. This work opens up a new avenue for designing antifreeze materials and provides peptide-based functional modules for synthetic biology.
Infections with multidrug-resistant (MDR) Gram-negative bacteria, such as MDR Escherichia coli (E. coli), remain a challenge due to the lack of safe antibiotics and high fatality rates under anti-infection therapies. This work presents a form of biomimetic intelligent catalysis inspired by the selective biocatalytic property of macrophages (MΦs), consisting of an intelligent controlling center (a living MΦ) and a Fenton reaction catalyst (Fe3O4@poly(lactic-co-glycolic acid) (PLGA) nanoparticles) for killing MDR E. coli without harming normal cells. The MΦ-Fe3O4@PLGA particles (i.e., the intelligent catalysis particles) exhibit selective biocatalysis activity toward MDR E. coli by producing H2O2 and lipid droplets (LDs). This process activates the lipid metabolism and glycan biosynthesis and metabolism pathways based on the result of RNA sequencing data analysis. The H2O2 further reacts with Fe3O4@PLGA to form highly toxic hydroxyl radicals (·OH), while the LDs contain antimicrobial peptides and can target MDR E. coli. The highly toxic ·OH and antimicrobial peptides are shown to combat with MDR E. coli, such that the antibacterial efficiency of the MΦ-Fe3O4@PLGA particles against MDR E. coli is 99.29% ± 0.31% in vitro. More importantly, after several passages, the intelligent catalysis function of the MΦ-Fe3O4@PLGA particles is well maintained. Hence, the concept of biomimetic intelligent catalysts displays potential for treating diseases other than infections.
Transplantation of probiotics to the intestine can positively regulate the gut microbiota, thereby promoting the immune system and treating various diseases. However, the harsh gastrointestinal environment and short retention time in the gastrointestinal tract significantly limit the bioavailability and intestinal colonization of probiotics. Herein, we present a double-layer polysaccharide hydrogel (DPH) in the form of a double-layer structure composed of a carboxymethyl cellulose (CMCL) supramolecular inner layer and a dialdehyde alginate (DAA) cross-linked carboxymethyl chitosan (CMCS) outer layer. This double-layer structure allows DPH to encapsulate and deliver probiotics in a targeted manner within the body. In the stomach, the cage structure of the DPH is closed, and the outer layer absorbs surrounding liquids to form a barrier to protect the probiotics from gastric fluids. In the intestine, the cage structure opens and disintegrates, releasing the probiotics. Thus, DPH endows probiotics with excellent intestine-targeted delivery, improved oral bioavailability, enhanced gastrointestinal tract tolerance, and robust mucoadhesion capacity. The encapsulated probiotics exhibit almost unchanged bioactivity in the gastrointestinal tract before release, as well as improved oral delivery. In particular, probiotics encapsulated by DPH exhibit 100.1 times higher bioavailability and 10.6 times higher mucoadhesion than free probiotics in an animal model 48 h post-treatment. In addition, with a remarkable ability to survive and be retained in the intestine, probiotics encapsulated by DPH show excellent in vitro and in vivo competition with pathogens. Notably, DAA-mediated dynamic crosslinking not only maintains the overall integrity of the hydrogels but also controls the release timing of the probiotics. Thus, it is expected that encapsulated substances (probiotics, proteins, etc.) can be delivered to specific sites of the intestinal tract by means of DPH, by controlling the dynamic covalent crosslinking.
The United Nations (UN)’s call for a decade of “ecosystem restoration” was prompted by the need to address the extensive impact of anthropogenic activities on natural ecosystems. Marine ecosystem restoration is increasingly necessary due to increasing habitat degredation in deep waters (>200 m depth). At these depths, which are far beyond those accessible by divers, only established and emerging robotic platforms such as remotely operated vehicles (ROVs), autonomous underwater vehicles (AUVs), landers, and crawlers can operate through manipulators and multiparametric sensor arrays (e.g., optoacoustic imaging, omics, and environmental probes). The use of advanced technologies for deep-sea ecosystem restoration can provide: ① high-resolution three-dimensional (3D) imaging and acoustic mapping of substrates and key taxa, ② physical manipulation of substrates and key taxa, ③ real-time supervision of remote operations and long-term ecological monitoring, and ④ the potential to work autonomously. Here, we describe how robotic platforms with in situ manipulation capabilities and payloads of innovative sensors could autonomously conduct active restoration and monitoring across large spatial scales. We expect that these devices will be particularly useful in deep-sea habitats, such as ① reef-building cold-water corals, ② soft-bottom bamboo corals, and ③ soft-bottom fishery resources that have already been damaged by offshore industries (i.e., fishing and oil/gas).
The Notch signaling pathway is evolutionarily conserved across metazoan species and plays key roles in many physiological processes. The Notch receptor is activated by two families of canonical ligands (Delta-like and Serrate/Jagged) where both ligands and receptors are single-pass transmembrane proteins usually with large extracellular domains, relative to their intracellular portions. Upon interaction of the core binding regions, presented on opposing cell surfaces, formation of the receptor/ligand complex initiates force-mediated proteolysis, ultimately releasing the transcriptionally-active Notch intracellular domain. This review focuses on structural features of the extracellular receptor/ligand complex, the role of post-translational modifications in tuning this complex, the contribution of the cell membrane to ligand function, and insights from acquired and genetic diseases.
To meet the goal of worldwide decarbonization, the transformation process toward clean and green energy structures has accelerated. In this context, coal-fired power plant (CFPP) and large-scale energy storage represented by compressed air energy storage (CAES) technology, are tasked with increasing renewable resource accommodation and maintaining the power system security. To achieve this, this paper proposes the concept of a CFPP-CAES combined cycle and a trigenerative system based on that. Considering the working conditions of the CFPP, thermal characteristics of three typical operation modes were studied and some general regularities were identified. The results of various potential integration schemes discussion indicated that extracting water from low-temperature points in the feedwater system to cool pressurized air and simultaneously increase the backwater temperature is beneficial for improving performance. In addition, preheating the pressurized air before the air expanders via low-grade water in the feedwater system as much as possible and reducing extracted steam contribute to increasing the efficiency. With the optimal integration scheme, 2.85 tonnes of coal can be saved per cycle and the round-trip efficiency can be increased by 2.24%. Through the cogeneration of heat and power, the system efficiency can reach 77.5%. In addition, the contribution degree of the three compression heat utilization methods to the performance improvement ranked from high to low, is preheating the feedwater before the boiler, supplying heat, and flowing into the CFPP feedwater system. In the cooling energy generation mode, the system efficiency can be increased to over 69%. Regardless of the operation mode, the benefit produced by integration is further enhanced when the CFPP operates at higher operating conditions because the coupling points parameters are changed. In addition, the dynamic payback period can be shortened by 11.33 years and the internal rate of return increases by 5.20% under a typical application scenario. Regarding the effect of different application scenarios in terms of economics, investing in the proposed system is more appropriate in regions with multiple energy demands, especially heating demand. These results demonstrate the technical advantages of the proposed system and provide guiding principles for its design, operation, and project investment.
Decarbonization of the electric power sector is essential for sustainable development. Low-carbon generation technologies, such as solar and wind energy, can replace the CO2-emitting energy sources (coal and natural gas plants). As a sustainable engineering practice, long-duration energy storage technologies must be employed to manage imbalances in the variable renewable energy supply and electricity demand. Compressed air energy storage (CAES) is an effective solution for balancing this mismatch and therefore is suitable for use in future electrical systems to achieve a high penetration of renewable energy generation. This study introduces recent progress in CAES, mainly advanced CAES, which is a clean energy technology that eliminates the use of fossil fuels, compared with two commercial CAES plants at Huntorf and McIntosh which are conventional ones utilizing fossil fuels. Advanced CAES include adiabatic CAES, isothermal CAES, liquid air energy storage, supercritical CAES, underwater CAES, and CAES coupled with other technologies. The principles and configurations of these advanced CAES technologies are briefly discussed and a comprehensive review of the state-of-the-art technologies is presented, including theoretical studies, experiments, demonstrations, and applications. The comparison and discussion of these CAES technologies are summarized with a focus on technical maturity, power sizing, storage capacity, operation pressure, round-trip efficiency, efficiency of the components, operation duration, and investment cost. Potential application trends were compiled. This paper presents a comprehensive reference for developing novel CAES systems and makes recommendations for future research and development to facilitate their application in several areas, ranging from fundamentals to applications.
