By combining machine learning with the design of experiments, thereby achieving so-called active machine learning, more efficient and cheaper research can be conducted. Machine learning algorithms are more flexible and are better than traditional design of experiment algorithms at investigating processes spanning all length scales of chemical engineering. While active machine learning algorithms are maturing, their applications are falling behind. In this article, three types of challenges presented by active machine learning—namely, convincing the experimental researcher, the flexibility of data creation, and the robustness of active machine learning algorithms—are identified, and ways to overcome them are discussed. A bright future lies ahead for active machine learning in chemical engineering, thanks to increasing automation and more efficient algorithms that can drive novel discoveries.
Polymeric materials with excellent performance are the foundation for developing high-level technology and advanced manufacturing. Polymeric material genome engineering (PMGE) is becoming a vital platform for the intelligent manufacturing of polymeric materials. However, the development of PMGE is still in its infancy, and many issues remain to be addressed. In this perspective, we elaborate on the PMGE concepts, summarize the state-of-the-art research and achievements, and highlight the challenges and prospects in this field. In particular, we focus on property estimation approaches, including property proxy prediction and machine learning prediction of polymer properties. The potential engineering applications of PMGE are discussed, including the fields of advanced composites, polymeric materials for communications, and integrated circuits.
Drug discovery and development affects various aspects of human health and dramatically impacts the pharmaceutical market. However, investments in a new drug often go unrewarded due to the long and complex process of drug research and development (R&D). With the advancement of experimental technology and computer hardware, artificial intelligence (AI) has recently emerged as a leading tool in analyzing abundant and high-dimensional data. Explosive growth in the size of biomedical data provides advantages in applying AI in all stages of drug R&D. Driven by big data in biomedicine, AI has led to a revolution in drug R&D, due to its ability to discover new drugs more efficiently and at lower cost. This review begins with a brief overview of common AI models in the field of drug discovery; then, it summarizes and discusses in depth their specific applications in various stages of drug R&D, such as target discovery, drug discovery and design, preclinical research, automated drug synthesis, and influences in the pharmaceutical market. Finally, the major limitations of AI in drug R&D are fully discussed and possible solutions are proposed.
The past decade has seen a sharp increase in machine learning (ML) applications in scientific research. This review introduces the basic constituents of ML, including databases, features, and algorithms, and highlights a few important achievements in chemistry that have been aided by ML techniques. The described databases include some of the most popular chemical databases for molecules and materials obtained from either experiments or computational calculations. Important two-dimensional (2D) and three-dimensional (3D) features representing the chemical environment of molecules and solids are briefly introduced. Decision tree and deep learning neural network algorithms are overviewed to emphasize their frameworks and typical application scenarios. Three important fields of ML in chemistry are discussed: ① retrosynthesis, in which ML predicts the likely routes of organic synthesis; ② atomic simulations, which utilize the ML potential to accelerate potential energy surface sampling; and ③ heterogeneous catalysis, in which ML assists in various aspects of catalytic design, ranging from synthetic condition optimization to reaction mechanism exploration. Finally, a prospect on future ML applications is provided.
Based on an analysis of the operational control behavior of operation experts on energy-intensive equipment, this paper proposes an intelligent control method for low-carbon operation by combining mechanism analysis with deep learning, linking control and optimization with prediction, and integrating decision-making with control. This method, which consists of setpoint control, self-optimized tuning, and tracking control, ensures that the energy consumption per tonne is as low as possible, while remaining within the target range. An intelligent control system for low-carbon operation is developed by adopting the end-edge-cloud collaboration technology of the Industrial Internet. The system is successfully applied to a fused magnesium furnace and achieves remarkable results in reducing carbon emissions.
The zinc oxide rotary kiln, as an essential piece of equipment in the zinc smelting industrial process, is presenting new challenges in process control. China’s strategy of achieving a carbon peak and carbon neutrality is putting new demands on the industry, including green production and the use of fewer resources; thus, traditional stability control is no longer suitable for multi-objective control tasks. Although researchers have revealed the principle of the rotary kiln and set up computational fluid dynamics (CFD) simulation models to study its dynamics, these models cannot be directly applied to process control due to their high computational complexity. To address these issues, this paper proposes a multi-objective adaptive optimization model predictive control (MAO-MPC) method based on sparse identification. More specifically, with a large amount of data collected from a CFD model, a sparse regression problem is first formulated and solved to obtain a reduction model. Then, a two-layered control framework including real-time optimization (RTO) and model predictive control (MPC) is designed. In the RTO layer, an optimization problem with the goal of achieving optimal operation performance and the lowest possible resource consumption is set up. By solving the optimization problem in real time, a suitable setting value is sent to the MPC layer to ensure that the zinc oxide rotary kiln always functions in an optimal state. Our experiments show the strength and reliability of the proposed method, which reduces the usage of coal while maintaining high profits.
Primary and metastatic lung cancers are malignant lung tumors each with of which has a different pathogenesis, although both threaten patient lives. Tumor development and progression involve communication between tumor cells and the host microenvironment. Neutrophils are the most abundant immune cells in the tumor microenvironment (TME); they participate in the generation of an inflammatory milieu and influence patient survival through their anti- and pro-tumor abilities. Neutrophils can be classified into various categories according to different criteria; frequent categories include N1 antitumor neutrophils and N2 immunosuppressive neutrophils. The antitumor effects of neutrophils are reported to be mediated through a combination of reactive oxygen species, tumor necrosis factor-related apoptosis-inducing ligand, and receptor for advanced glycation end-products-cathepsin G association, as well as the regulation of the activities of other immune cells. There have also been reports that neutrophils can function as tumor promoters that contribute to lung cancer progression and metastasis by influencing processes including carcinogenesis, angiogenesis, cancer cell proliferation, and invasion ability, as well as having similar roles in the lung metastasis of other cancers. The rapid development of nanotechnology has provided new strategies for cancer treatment targeting neutrophils.
Recombinant adenovirus serotype 5 (Ad5) vector has been widely applied in vaccine development targeting infectious diseases, such as Ebola virus disease and coronavirus disease 2019 (COVID-19). However, the high prevalence of preexisting anti-vector immunity compromises the immunogenicity of Ad5-based vaccines. Thus, there is a substantial unmet need to minimize preexisting immunity while improving the insert-induced immunity of Ad5 vectors. Herein, we address this need by utilizing biocompatible nanoparticles to modulate Ad5-host interactions. We show that positively charged human serum albumin nanoparticles ((+)HSAnp), which are capable of forming a complex with Ad5, significantly increase the transgene expression of Ad5 in both coxsackievirus-adenovirus receptor-positive and -negative cells. Furthermore, in charge- and dose-dependent manners, Ad5/(+)HSAnp complexes achieve robust (up to 227-fold higher) and long-term (up to 60 days) transgene expression in the lungs of mice following intranasal instillation. Importantly, in the presence of preexisting anti-Ad5 immunity, complexed Ad5-based Ebola and COVID-19 vaccines significantly enhance antigen-specific humoral response and mucosal immunity. These findings suggest that viral aggregation and charge modification could be leveraged to engineer enhanced viral vectors for vaccines and gene therapies.
Subpixel localization techniques for estimating the positions of point-like images captured by pixelated image sensors have been widely used in diverse optical measurement fields. With unavoidable imaging noise, there is a precision limit (PL) when estimating the target positions on image sensors, which depends on the detected photon count, noise, point spread function (PSF) radius, and PSF’s intra-pixel position. Previous studies have clearly reported the effects of the first three parameters on the PL but have neglected the intra-pixel position information. Here, we develop a localization PL analysis framework for revealing the effect of the intra-pixel position of small PSFs. To accurately estimate the PL in practical applications, we provide effective PSF (ePSF) modeling approaches and apply the Cramér-Rao lower bound. Based on the characteristics of small PSFs, we first derive simplified equations for finding the best PL and the best intra-pixel region for an arbitrary small PSF; we then verify these equations on real PSFs. Next, we use the typical Gaussian PSF to perform a further analysis and find that the final optimum of the PL is achieved at the pixel boundaries when the Gaussian radius is as small as possible, indicating that the optimum is ultimately limited by light diffraction. Finally, we apply the maximum likelihood method. Its combination with ePSF modeling allows us to successfully reach the PL in experiments, making the above theoretical analysis effective. This work provides a new perspective on combining image sensor position control with PSF engineering to make full use of information theory, thereby paving the way for thoroughly understanding and achieving the final optimum of the PL in optical localization.
Accurate and effective identification of adverse geology is crucial for safe and efficient tunnel construction. Current methods of identifying adverse geology depend on the experience of geologists and are prone to misjudgment and omissions. Here, we propose a method for adverse geology identification in tunnels based on mineral anomaly analysis. The method is based on the theory of geoanomaly, and the mineral anomalies are geological markers of the presence of adverse geology. The method uses exploration data analysis (EDA) to calculate mineral anomaly thresholds, then evaluates the mineral anomalies based on the thresholds and identifies adverse geology based on the characteristics of the mineral anomalies. We have established a dynamic expansion process for background samples to achieve the dynamic evaluation of mineral anomalies by adjusting anomaly thresholds. This method has been validated and applied in a tunnel excavated in granite. As shown herein, in the tunnel range of 142 + 800-142 + 860, the fault F37 was successfully identified based on an anomalous decrease in the diagenetic minerals plagioclase and hornblende, as well as an anomalous increase in the content of the alteration minerals chlorite, laumonite, and epidote. The proposed method provides a timely warning when a tunnel enters areas affected by adverse geology and identifies whether the tunnel is gradually approaching or moving away from the fault. In addition, the applicability, accuracy, and further improvement of the method are discussed. This method improves our ability to identify adverse geology, from qualitative to quantitative, and can provide reference and guidance for the identification of adverse geology in mining and underground engineering.
As a core infrastructure of high-speed railways, ballast layers constituted by graded crushed stones feature noteworthy particle movement compared with normal railways, which may cause excessive settlement and have detrimental effects on train operation. However, the movement behavior remains ambiguous due to a lack of effective measurement approaches and analytical methods. In this study, an image-aided technique was developed in a full-scale model test using digital cameras and a color-based identification approach. A total of 1274 surface ballast particles were manually dyed by discernible colors to serve as tracers in the test. The movements of the surface ballast particles were tracked using the varied pixels displaying tracers in the photos that were intermittently taken during the test in the perpendicular direction. The movement behavior of ballast particles under different combinations of train speeds and axle loads was quantitatively evaluated. The obtained results indicated that the surface ballast particle movements were slight, mainly concentrated near sleepers under low-speed train loads and greatly amplified and extended to the whole surface when the train speed reached 360 km·h−1. Additionally, the development of ballast particle displacement statistically resembled its rotation. Track vibration contributed to the movements of ballast particles, which specifically were driven by vertical acceleration near the track center and horizontal acceleration at the track edge. Furthermore, the development trends of ballast particle movements and track settlement under long-term train loading were similar, and both stabilized at nearly the same time. The track performance, including the vibration characteristics, accumulated settlement, and sleeper support stiffness, was determined to be closely related to the direction and distribution of ballast particle flow, which partly deteriorated under high-speed train loads.
Recalcitrance and the inherent heterogeneity of lignin structure are the major bottlenecks to impede the popularization of lignin-based chemicals production processes. Recent works suggested a promising pathway for lignin depolymerization and lignin-derived bio-oil upgrading via an electrochemical biorefinery (a process in which lignin valorization is performed via electrochemical oxidation or reduction). This review presents the progress on chemicals synthesis and bio-oil upgrading from lignin by an electrochemical biorefinery, relating to the lignin biosynthesis pathway, reaction pathway of lignin electrochemical conversion, inner-sphere and outer-sphere electron transfer mechanism, basic kinetics and thermodynamics in electrochemistry, and the recent embodiments analysis with the emphasis on the respective feature and limitation for lignin electrochemical oxidative and reductive conversion. Lastly, the challenge and perspective associated with lignin electrochemical biorefinery are discussed. Present-day results indicate that more work should be performed to promote efficiency, selectivity, and stability in pursuing a lignin electrochemical biorefinery. One of the most promising developing directions appears to be integrating various types of lignin electrochemical conversion strategies and other existing or evolving lignin valorization technologies. This review aims to provide more references and discussion on the development for lignin electrochemical biorefinery.
Heavy metals, notably Pb2+ and Cu2+, are some of the most persistent contaminants found in groundwater. Frequent monitoring of these metals, which relies on efficient, sensitive, cost-effective, and reliable methods, is a necessity. We present a nanocomposite-based miniaturized electrode for the concurrent measurement of Pb2+ and Cu2+ by exploiting the electroanalytical technique of square wave voltammetry. We also propose a facile in situ hydrothermal calcination method to directly grow binder-free mesoporous NiO on a three-dimensional nickel foam, which is then electrochemically seeded with gold nanoparticles (AuNPs). The meticulous design of a low-barrier Ohmic contact between mesoporous NiO and AuNPs facilitates target-mediated nanochannel-confined electron transfer within mesoporous NiO. As a result, the heavy metals Pb2+ (0.020 mg·L−1 detection limit; 2.0-16.0 mg·L−1 detection range) and Cu2+ (0.013 mg·L−1 detection limit; 0.4-12.8 mg·L−1 detection range) can be detected simultaneously with high precision. Furthermore, other heavy metal ions and common interfering ions found in groundwater showed negligible impacts on the electrode’s performance, and the recovery rate of groundwater samples varied between 96.3% ± 2.1% and 109.4% ± 0.6%. The compactness, flexible shape, low power consumption, and ability to remotely operate our electrode pave the way for onsite detection of heavy metals in groundwater, thereby demonstrating the potential to revolutionize the field of environmental monitoring.
Check-dam construction is an effective and widely used method for sediment trapping in the Yellow River Basin and other places over the world that are prone to severe soil erosion. Quantitative estimations of the dynamic sediment trapped by check dams are necessary for evaluating the effects of check dams and planning the construction of new ones. In this study, we propose a new framework, named soil and water assessment tool (SWAT)-dynamic check dam (DCDam), for modeling the sediment trapped by check dams dynamically, by integrating the widely utilized SWAT model and a newly developed module called DCDam. We then applied this framework to a typical loess watershed, the Yan River Basin, to assess the time-varying effects of check-dam networks over the past 60 years (1957-2016). The DCDam module generated a specific check-dam network to conceptualize the complex connections at each time step (monthly). In addition, the streamflow and sediment load simulated by using the SWAT model were employed to force the sediment routing in the check-dam network. The evaluation results revealed that the SWAT-DCDam framework performed satisfactorily, with an overestimation of 11.50%, in simulating sediment trapped by check dams, when compared with a field survey of the accumulated sediment deposition. For the Yan River Basin, our results indicated that the designed structural parameters of check dams have evolved over the past 60 years, with higher dams (37.14% and 9.22% increase for large dams and medium dams, respectively) but smaller controlled areas (46.03% and 10.56% decrease for large dams and medium dams, respectively) in recent years. Sediment retained by check dams contributed to approximately 15.00% of the total sediment load reduction in the Yan River during 1970-2016. Thus, our developed framework can be a promising tool for evaluating check-dam effects, and this study can provide valuable information and support to decision-making for soil and water conservation and check-dam planning and management.
Strategies aimed at defining, discovering, and developing alternatives to traditional antibiotics will underlie the development of sustainable agricultural systems. Among such strategies, antimicrobial peptides (AMPs) with broad-spectrum antimicrobial activity and multifaceted mechanisms of action are recognized as ideal alternatives in the post-antibiotic era. In particular, AMPs derived from microbes with active metabolisms that can adapt to a variety of extreme environments have long been sought after. Consequently, this review summarizes information on naturally occurring AMPs, including their biological activity, antimicrobial mechanisms, and the preparation of microbial-derived AMPs; it also outlines their applications and the challenges presented by their use in the agroindustry. By dissecting the research results on microbial-derived AMPs of previous generations, this study contributes valuable knowledge on the exploration and realization of the applications of AMPs in sustainable agriculture.
Antibiotic resistance, which is encoded by antibiotic-resistance genes (ARGs), has proliferated to become a growing threat to public health around the world. With technical advances, especially in the popularization of metagenomic sequencing, scientists have gained the ability to decipher the profiles of ARGs in diverse samples with high accuracy at an accelerated speed. To analyze thousands of ARGs in a high-throughput way, standardized and integrated pipelines are needed. The new version (v3.0) of the widely used ARGs online analysis pipeline (ARGs-OAP) has made significant improvements to both the reference database—the structured ARG (SARG) database—and the integrated analysis pipeline. SARG has been enhanced with sequence curation to improve annotation reliability, incorporate emerging resistance genotypes, and determine rigorous mechanism classification. The database has been further organized and visualized online in the format of a tree-like structure with a dictionary. It has also been divided into sub-databases for different application scenarios. In addition, the ARGs-OAP has been improved with adjusted quantification methods, simplified tool implementation, and multiple functions with user-defined reference databases. Moreover, the online platform now provides a diverse biostatistical analysis workflow with visualization packages for the efficient interpretation of ARG profiles. The ARGs-OAP v3.0 with an improved database and analysis pipeline will benefit academia, governmental management, and consultation regarding risk assessment of the environmental prevalence of ARGs.
The robot pilot is a new concept of a robot system that pilots a manned aircraft, thereby forming a new type of unmanned aircraft system (UAS) that makes full use of the platform maturity, load capacity, and airworthiness of existing manned aircraft while greatly expanding the operation and application fields of UASs. In this research, the implementation and advantages of the robot pilot concept are discussed in detail, and a helicopter robot pilot is proposed to fly manned helicopters. The robot manipulators are designed according to the handling characteristics of the helicopter-controlling mechanism. Based on a kinematic analysis of the robot manipulators, a direct-driving method is established for the robot flight controller to reduce the time delay and control error of the robot servo process. A supporting ground station is built to realize different flight modes and the functional integration of the robot pilot. Finally, a prototype of the helicopter robot pilot is processed and installed in a helicopter to carry out flight tests. The test results show that the robot pilot can independently fly the helicopter to realize forward flight, backward flight, side flight, and turning flight, which verifies the effectiveness of the helicopter robot pilot.
Although numerous advances have been made in information technology in the past decades, there is still a lack of progress in information systems dynamics (ISD), owing to the lack of a mathematical foundation needed to describe information and the lack of an analytical framework to evaluate information systems. The value of ISD lies in its ability to guide the design, development, application, and evaluation of large-scale information system-of-systems (SoSs), just as mechanical dynamics theories guide mechanical systems engineering. This paper reports on a breakthrough in these fundamental challenges by proposing a framework for information space, improving a mathematical theory for information measurement, and proposing a dynamic configuration model for information systems. In this way, it establishes a basic theoretical framework for ISD. The proposed theoretical methodologies have been successfully applied and verified in the Smart Court SoSs Engineering Project of China and have achieved significant improvements in the quality and efficiency of Chinese court informatization. The proposed ISD provides an innovative paradigm for the analysis, design, development, and evaluation of large-scale complex information systems, such as electronic government and smart cities.
Safety is essential when building a strong transportation system. As a key development direction in the global railway system, the intelligent railway has safety at its core, making safety a top priority while pursuing the goals of efficiency, convenience, economy, and environmental friendliness. This paper describes the state of the art and proposes a system architecture for intelligent railway systems. It also focuses on the development of railway safety technology at home and abroad, and proposes the active safety method and technology system based on advanced theoretical methods such as the in-depth integration of cyber-physical systems (CPS), data-driven models, and intelligent computing. Finally, several typical applications are demonstrated to verify the advancement and feasibility of active safety technology in intelligent railway systems.
