Engineering最新文献

Wastewater surveillance (WWS) can leverage its wide coverage, population-based sampling, and high monitoring frequency to capture citywide pandemic trends independent of clinical surveillance. Here we conducted a nine months daily WWS for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) from 12 wastewater treatment plants (WWTPs), covering approximately 80% of the population, to monitor infection dynamics in Hong Kong, China. We found that the SARS-CoV-2 virus concentration in wastewater was correlated with the daily number of reported cases and reached two pandemic peaks three days earlier during the study period. In addition, two different methods were established to estimate the prevalence/incidence rates from wastewater measurements. The estimated results from wastewater were consistent with findings from two independent citywide clinical surveillance programmes (rapid antigen test (RAT) surveillance and serology surveillance), but higher than the cases number reported by the Centre for Health Protection (CHP) of Hong Kong, China. Moreover, the effective reproductive number (R_t) was estimated from wastewater measurements to reflect both citywide and regional transmission dynamics. Our findings demonstrate that large-scale intensive WWS from WWTPs provides cost-effective and timely public health information, especially when the clinical surveillance is inadequate and costly. This approach also provides insights into pandemic dynamics at higher spatiotemporal resolutions, facilitating the formulation of effective control policies and targeted resource allocation.

This article reviews the anti-penetration principles and strengthening mechanisms of metal materials, ranging from macroscopic failure modes to microscopic structural characteristics, and further summarizes the micro–macro correlation in the anti-penetration process. Finally, it outlines the constitutive models and numerical simulation studies utilized in the field of impact and penetration. From the macro perspective, nine frequent penetration failure modes of metal materials are summarized, with a focus on the analysis of the cratering, compression shear, penetration, and plugging stages of the penetration process. The reasons for the formation of adiabatic shear bands (ASBs) in metal materials with different crystal structures are elaborated, and the formation mechanism of the equiaxed grains in the ASB is explored. Both the strength and the toughness of metal materials are related to the materials’ crystal structures and microstructures. The toughness is mainly influenced by the deformation mechanism, while the strength is explained by the strengthening mechanism. Therefore, the mechanical properties of metal materials depend on their microstructures, which are subject to the manufacturing process and material composition. Regarding numerical simulation, the advantages and disadvantages of different constitutive models and simulation methods are summarized based on the application characteristics of metal materials in high-speed penetration practice. In summary, this article provides a systematic overview of the macroscopic and microscopic characteristics of metal materials, along with their mechanisms and correlation during the anti-penetration and impact-resistance processes, thereby making an important contribution to the scientific understanding of anti-penetration performance and its optimization in metal materials.

Optical imaging in the second near-infrared (NIR-II; 900–1880 nm) window is currently a popular research topic in the field of biomedical imaging. This study aimed to explore the application value of NIR-II fluorescence imaging in foot and ankle surgeries. A lab-established NIR-II fluorescence surgical navigation system was developed and used to navigate foot and ankle surgeries which enabled obtaining more high-spatial-frequency information and a higher signal-to-background ratio (SBR) in NIR-II fluorescence images compared to NIR-I fluorescence images; our result demonstrates that NIR-II imaging could provide higher-contrast and larger-depth images to surgeons. Three types of clinical application scenarios (diabetic foot, calcaneal fracture, and lower extremity trauma) were included in this study. Using the NIR-II fluorescence imaging technique, we observed the ischemic region in the diabetic foot before morphological alterations, accurately determined the boundary of the ischemic region in the surgical incision, and fully assessed the blood supply condition of the flap. NIR-II fluorescence imaging can help surgeons precisely judge surgical margins, detect ischemic lesions early, and dynamically trace the perfusion process. We believe that portable and reliable NIR-II fluorescence imaging equipment and additional functional fluorescent probes can play crucial roles in precision surgery.

Underground salt cavern CO₂ storage (SCCS) offers the dual benefits of enabling extensive CO₂ storage and facilitating the utilization of CO₂ resources while contributing the regulation of the carbon market. Its economic and operational advantages over traditional carbon capture, utilization, and storage (CCUS) projects make SCCS a more cost-effective and flexible option. Despite the widespread use of salt caverns for storing various substances, differences exist between SCCS and traditional salt cavern energy storage in terms of gas-tightness, carbon injection, brine extraction control, long-term carbon storage stability, and site selection criteria. These distinctions stem from the unique phase change characteristics of CO₂ and the application scenarios of SCCS. Therefore, targeted and forward-looking scientific research on SCCS is imperative. This paper introduces the implementation principles and application scenarios of SCCS, emphasizing its connections with carbon emissions, carbon utilization, and renewable energy peak shaving. It delves into the operational characteristics and economic advantages of SCCS compared with other CCUS methods, and addresses associated scientific challenges. In this paper, we establish a pressure equation for carbon injection and brine extraction, that considers the phase change characteristics of CO₂, and we analyze the pressure during carbon injection. By comparing the viscosities of CO₂ and other gases, SCCS’s excellent sealing performance is demonstrated. Building on this, we develop a long-term stability evaluation model and associated indices, which analyze the impact of the injection speed and minimum operating pressure on stability. Field countermeasures to ensure stability are proposed. Site selection criteria for SCCS are established, preliminary salt mine sites suitable for SCCS are identified in China, and an initial estimate of achievable carbon storage scale in China is made at over 51.8–77.7 million tons, utilizing only 20%–30% volume of abandoned salt caverns. This paper addresses key scientific and engineering challenges facing SCCS and determines crucial technical parameters, such as the operating pressure, burial depth, and storage scale, and it offers essential guidance for implementing SCCS projects in China.

The traditional method of screening plants for disease resistance phenotype is both time-consuming and costly. Genomic selection offers a potential solution to improve efficiency, but accurately predicting plant disease resistance remains a challenge. In this study, we evaluated eight different machine learning (ML) methods, including random forest classification (RFC), support vector classifier (SVC), light gradient boosting machine (lightGBM), random forest classification plus kinship (RFC_K), support vector classification plus kinship (SVC_K), light gradient boosting machine plus kinship (lightGBM_K), deep neural network genomic prediction (DNNGP), and densely connected convolutional networks (DenseNet), for predicting plant disease resistance. Our results demonstrate that the three plus kinship (K) methods developed in this study achieved high prediction accuracy. Specifically, these methods achieved accuracies of up to 95% for rice blast (RB), 85% for rice black-streaked dwarf virus (RBSDV), and 85% for rice sheath blight (RSB) when trained and applied to the rice diversity panel I (RDPI). Furthermore, the plus K models performed well in predicting wheat blast (WB) and wheat stripe rust (WSR) diseases, with mean accuracies of up to 90% and 93%, respectively. To assess the generalizability of our models, we applied the trained plus K methods to predict RB disease resistance in an independent population, rice diversity panel II (RDPII). Concurrently, we evaluated the RB resistance of RDPII cultivars using spray inoculation. Comparing the predictions with the spray inoculation results, we found that the accuracy of the plus K methods reached 91%. These findings highlight the effectiveness of the plus K methods (RFC_K, SVC_K, and lightGBM_K) in accurately predicting plant disease resistance for RB, RBSDV, RSB, WB, and WSR. The methods developed in this study not only provide valuable strategies for predicting disease resistance, but also pave the way for using machine learning to streamline genome-based crop breeding.

To reduce CO₂ emissions from coal-fired power plants, the development of low-carbon or carbon-free fuel combustion technologies has become urgent. As a new zero-carbon fuel, ammonia (NH₃) can be used to address the storage and transportation issues of hydrogen energy. Since it is not feasible to completely replace coal with ammonia in the short term, the development of ammonia–coal co-combustion technology at the current stage is a fast and feasible approach to reduce CO₂ emissions from coal-fired power plants. This study focuses on modifying the boiler and installing two layers of eight pure-ammonia burners in a 300-MW coal-fired power plant to achieve ammonia–coal co-combustion at proportions ranging from 20% to 10% (by heat ratio) at loads of 180- to 300-MW, respectively. The results show that, during ammonia–coal co-combustion in a 300-MW coal-fired power plant, there was a more significant change in NO_x emissions at the furnace outlet compared with that under pure-coal combustion as the boiler oxygen levels varied. Moreover, ammonia burners located in the middle part of the main combustion zone exhibited a better high-temperature reduction performance than those located in the upper part of the main combustion zone. Under all ammonia co-combustion conditions, the NH₃ concentration at the furnace outlet remained below 1 parts per million (ppm). Compared with that under pure-coal conditions, the thermal efficiency of the boiler slightly decreased (by 0.12%–0.38%) under different loads when ammonia co-combustion reached 15 t·h⁻¹. Ammonia co-combustion in coal-fired power plants is a potentially feasible technology route for carbon reduction.

Hydrogen has emerged as a promising alternative to meet the growing demand for sustainable and renewable energy sources. Underground hydrogen storage (UHS) in depleted gas reservoirs holds significant potential for large-scale energy storage and the seamless integration of intermittent renewable energy sources, due to its capacity to address challenges associated with the intermittent nature of renewable energy sources, ensuring a steady and reliable energy supply. Leveraging the existing infrastructure and well-characterized geological formations, depleted gas reservoirs offer an attractive option for large-scale hydrogen storage implementation. However, significant knowledge gaps regarding storage performance hinder the commercialization of UHS operation. Hydrogen deliverability, hydrogen trapping, and the equation of state are key areas with limited understanding. This literature review critically analyzes and synthesizes existing research on hydrogen storage performance during underground storage in depleted gas reservoirs; it then provides a high-level risk assessment and an overview of the techno-economics of UHS. The significance of this review lies in its consolidation of current knowledge, highlighting unresolved issues and proposing areas for future research. Addressing these gaps will advance hydrogen-based energy systems and support the transition to a sustainable energy landscape. Facilitating efficient and safe deployment of UHS in depleted gas reservoirs will assist in unlocking hydrogen’s full potential as a clean and renewable energy carrier. In addition, this review aids policymakers and the scientific community in making informed decisions regarding hydrogen storage technologies.

Lianhua Qingke tablets, a patented traditional Chinese medicine that has validated clinical efficacy for treating cough caused by severe acute respiratory syndrome coronavirus 2 infection, lack rigorous evidence-based research evaluating their effect on long coronavirus disease (COVID) cough. A randomized, double-blind, placebo-controlled, multicenter clinical study was conducted among patients with long COVID cough from 19 hospitals and 23 community health centers in China. Patients were randomized 1:1 to receive either Lianhua Qingke tablets or placebo orally for 14 days (four tablets, 1.84 g, three times a day). The primary endpoint indicator was the disappearance of cough, with the remission of cough also considered. Among 482 randomized patients, 480 (full analysis set 480; per-protocol set 470; safety set 480) were included in the primary analysis. According to the full analysis, the time until cough disappearance was significantly shorter in the trial group than in the control group, with a significant increase in the 14-day cough disappearance rate. Accordingly, the time to cough remission was significantly shorter in the trial group than in the control group. The change in the total symptom score was significantly greater in the trial group than in the control group on days 7 and 14, consistent with the results indicated by the visual analog scale (VAS) and cough evaluation test (CET) scores. No serious adverse events were recorded during the study. Lianhua Qingke tablets significantly improved the clinical symptoms of patients with long COVID cough.

This study demonstrates the feasibility and effectiveness of utilizing native soils as a resource for inocula to produce n-caproate through the chain elongation (CE) platform, offering new insights into anaerobic soil processes. The results reveal that all five of the tested soil types exhibit CE activity when supplied with high concentrations of ethanol and acetate, highlighting the suitability of soil as an ideal source for n-caproate production. Compared with anaerobic sludge and pit mud, the native soil CE system exhibited higher selectivity (60.53%), specificity (82.32%), carbon distribution (60.00%), electron transfer efficiency (165.00%), and conductivity (0.59 ms∙cm⁻¹). Kinetic analysis further confirmed the superiority of soil in terms of a shorter lag time and higher yield. A microbial community analysis indicated a positive correlation between the relative abundances of Pseudomonas, Azotobacter, and Clostridium and n-caproate production. Moreover, metagenomics analysis revealed a higher abundance of functional genes in key microbial species, providing direct insights into the pathways involved in n-caproate formation, including in situ CO₂ utilization, ethanol oxidation, fatty acid biosynthesis (FAB), and reverse beta-oxidation (RBO). The numerous functions in FAB and RBO are primarily associated with Pseudomonas, Clostridium, Rhodococcus, Stenotrophomonas, and Geobacter, suggesting that these genera may play roles that are involved or associated with the CE process. Overall, this innovative inoculation strategy offers an efficient microbial source for n-caproate production, underscoring the importance of considering CE activity in anaerobic soil microbial ecology and holding potential for significant economic and environmental benefits through soil consortia exploration.

In this paper, we propose mesoscience-guided deep learning (MGDL), a deep learning modeling approach guided by mesoscience, to study complex systems. When establishing sample dataset based on the same system evolution data, different from the operation of conventional deep learning method, MGDL introduces the treatment of the dominant mechanisms of complex system and interactions between them according to the principle of compromise in competition (CIC) in mesoscience. Mesoscience constraints are then integrated into the loss function to guide the deep learning training. Two methods are proposed for the addition of mesoscience constraints. The physical interpretability of the model-training process is improved by MGDL because guidance and constraints based on physical principles are provided. MGDL was evaluated using a bubbling bed modeling case and compared with traditional techniques. With a much smaller training dataset, the results indicate that mesoscience-constraint-based model training has distinct advantages in terms of convergence stability and prediction accuracy, and it can be widely applied to various neural network configurations. The MGDL approach proposed in this paper is a novel method for utilizing the physical background information during deep learning model training. Further exploration of MGDL will be continued in the future.