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Based on the practical problem that excessive effluent pollutants caused by the impact of soybean wastewater on urban sewage treatment system, the effect of soybean wastewater on pollutants removal in sequencing batch reactor (SBR) and emergency control measures were investigated through single cycle instantaneous wastewater impact test. The results showed that with the increase of water quality impact load, COD removal rate increased initially and then decreased, while NH4+-N and TN removal rates increased gradually. The effect of water quality impact on TP removal was relatively small. When COD of the impact wastewater was 370 mg/L, the effluent NH4+-N and TN could not meet the first level class A standard of Discharge Standard of Pollutants for Municipal Wastewater Treatment Plant (GB 18918-2002). As the water volume impact increased, the changes of COD and TN removal rates remained consistent with the water quality impact, and the effect of water volume impact on NH4+-N and TP removal was relatively small. As the impact of water quality and water volume increased, the protein(PN) and polysaccharide(PS) components in extracellular polymeric substances(EPS) were both affected. Regulating the aeration rate could be used as an effective emergency control measure for the impact of soybean products wastewater. When the COD of the impact wastewater was 370 mg/L and the aeration rate was 0.9 L/min, COD, NH4+-N, TN, and TP could all meet the discharge standards. The results could provide reference for coping with the impact of other loaded soybean products wastewater.

Abstract Paddy fields are hotspots of microbial denitrification, which is typically linked to the oxidation of electron donors such as methane (CH4) under anoxic and hypoxic conditions. While several anaerobic methanotrophs can facilitate denitrification intracellularly, whether and how aerobic CH4 oxidation couples with denitrification in hypoxic paddy fields remains virtually unknown. Here we combine a ~3300 km field study across main rice-producing areas of China and 13CH4-DNA-stable isotope probing (SIP) experiments to investigate the role of soil aerobic CH4 oxidation in supporting denitrification. Our results reveal positive relationships between CH4 oxidation and denitrification activities and genes across various climatic regions. Microcosm experiments confirm that CH4 and methanotroph addition promote gene expression involved in denitrification and increase nitrous oxide emissions. Moreover, 13CH4-DNA-SIP analyses identify over 70 phylotypes harboring genes associated with denitrification and assimilating 13C, which are mostly belonged to Rubrivivax, Magnetospirillum, and Bradyrhizobium. Combined analyses of 13C-metagenome-assembled genomes and 13C-metabolomics highlight the importance of intermediates such as acetate, propionate and lactate, released during aerobic CH4 oxidation, for the coupling of CH4 oxidation with denitrification. Our work identifies key microbial taxa and pathways driving coupled aerobic CH4 oxidation and denitrification, with important implications for nitrogen management and greenhouse gas regulation in agroecosystems.

Abstract Platinum‐based alloy nanoparticles are the most attractive catalysts for the oxygen reduction reaction at present, but an in‐depth understanding of the relationship between their short‐range structural information and catalytic performance is still lacking. Herein, we present a synthetic strategy that uses transition‐metal oxide‐assisted thermal diffusion. PtCo/C catalysts with localized tetragonal distortion were obtained by controlling the thermal diffusion process of transition‐metal elements. This localized structural distortion induced a significant strain effect on the nanoparticle surface, which further shortened the length of the Pt–Pt bond, improved the electronic state of the Pt surface, and enhanced the performance of the catalyst. PtCo/C catalysts with special short‐range structures achieved excellent mass activity (2.27 A mgPt−1) and specific activity (3.34 A cm−2). In addition, the localized tetragonal distortion‐induced surface compression of the Pt skin improved the stability of the catalyst. The mass activity decreased by only 13% after 30,000 cycles. Enhanced catalyst activity and excellent durability have also been demonstrated in the proton exchange membrane fuel cell configuration. This study provides valuable insights into the development of advanced Pt‐based nanocatalysts and paves the way for reducing noble‐metal loading and increasing the catalytic activity and catalyst stability.

Abstract Introduction Farmlands are key sources of greenhouse gas (GHG) emissions, which are susceptible to changes in precipitation regimes. The soils of seasonal fallow contribute approximately half of annual GHG emissions from farmlands, but the effect of precipitation frequency on soil GHG emissions from seasonal fallow croplands remains virtually unknown. Materials and Methods We conducted a microcosm study to evaluate the response of nitrous oxide (N2O), methane (CH4) and carbon dioxide (CO2) fluxes from typical paddy and upland soils to the changes in watering frequency simulating precipitation scenarios of subtropical regions during seasonal fallow. We also analyzed changes of soil properties and biotic characteristics associated with GHG emissions, including abundances of soil denitrifiers (nirK, nirS, nosZI and nosZII genes), methanotrophs (pmoA gene) and methanogens (mcrA gene) to altered watering frequency. Results Increased watering frequency led to overall increases in soil N2O and CO2 fluxes compared with low frequency. Compared with low frequency, high watering frequency decreased CH4 flux from the paddy soil by 3.5 times, while enhanced CH4 flux from the upland soil by 60%. Furthermore, the increased watering frequency had positive effects on cumulative N2O and CO2 fluxes from the upland soil, whereas no similar trend was observed for the paddy soil. Hierarchical partitioning analyses showed that N2O fluxes from the paddy soil were mostly related to nitrogen availability, and mcrA gene abundance had more than 90% of relative independent effects on CH4 and CO2 fluxes from the paddy soil. For the upland soil, nosZ (60.34%), pmoA (53.18%) and nir (47.07%) gene abundances were important predictors of N2O, CH4 and CO2 fluxes, respectively. Conclusion Our results demonstrate that increased watering frequency facilitates GHG emissions by changing soil properties and functional gene abundances. These findings provide new insights into GHG fluxes from seasonal fallow croplands in response to altered precipitation patterns.

With the advancement of automobile lightweight, hot-dip galvanized low alloy high-strength steel strips are widely used in automobile structural parts or reinforcement due to their high yield strength and good forming, welding and anti-corrosion properties. Based on the research and development of hot-dip galvanized HC420LAD+Z steel strip in a steel plant, a low-cost and high-quality hot-dip galvanized HC420LAD+Z steel strip for automobile was developed from the aspects of process route design, reasonable control of chemical composition and optimization of key production process. The strip possessed good mechanical properties and adhesion to zinc layer, with the yield strength of 432~484 MPa, the tensile strength of 541~588 MPa, and the elongation A80 of 20.5~26.0%. Furthermore, the strip had batch stable production capacity, which provided a good reference for the industrial production of automotive high-strength steel.

Abstract Background Trans-hepatic arterial chemoembolization (TACE) is a treatment option for liver cancer patients. It can prolong patients’ survival but can also cause symptom distress. Symptom distress (SDs) can directly impact quality of life (QOL) and may indirectly influence QOL by lessening hope. In this study, we wanted to explore the mediating effect of hope on the relationship between SDs and QOL among patients with liver cancer receiving TACE. Methods A cross-sectional study was conducted from December 20, 2017, to August 6, 2018, at a gastroenterology ward of a medical center. The participants were 92 liver cancer patients (69.6% male, mean age 67.8) who were admitted for TACE treatment. Information on SDs, hope, and QOL was collected by questionnaire on discharge day. Hayes’ PROCESS model was used to test the mediating effect of hope on the relationship between SDs and QOL. Results The mean score and standard deviation (SD) of SDs, hope, and QOL were 32.08 (SD = 6.22), 27.09 (SD = 3.51), and 55.16 (SD = 17.33), respectively. SDs negatively impacts quality of life. The total effect of SDs on QOL was − 1.41 (95% confidence interval [CI]: − 1.96, − 0.86). The indirect effect via the mediation of hope was − 0.95 (95% CI: − 1.7, − 0.45). Hope partially mediated the effect of SDs on QOL. Conclusion SDs after TACE is vital; it directly reduces a patient’s overall QOL and can indirectly hinder it by reducing the patient’s hope. In addition to symptom management, interventions that help patients maintain their hope are key to improving QOL among patients receiving TACE.

The status of axillary lymph nodes is important for the staging, treatment, and prognosis of breast cancer. The arrival of the era of precision medicine highlights the importance of axillary lymph nodes status assessment. Magnetic resonance imaging, positron emission tomography/computed tomography, and new imaging examinations can be used in the assessment of axillary lymph nodes, in addition to routine examinations, such as physical examination and breast ultrasound. Sentinel lymph node biopsy is the most significant method for the assessment of axillary lymph nodes. Emerging evidence suggests that axillary lymph node dissection can be avoided in patients with negative or 1–2 positive sentinel lymph nodes. The ratio of inflammatory indicators may be a reliable predictor of lymph node metastases. Specific elder patients can have a good prognosis without axillary procedure. In this paper, we review and summarize the controversies surrounding the assessment methods and management of axillary lymph nodes. We hope to provide new perspectives for the axillary lymph node diagnosis and management of breast cancer patients under the concept of precision therapy.

Abstract Background Diagnostic performance of optical coherence tomography (OCT) to detect Alzheimer’s disease (AD) and mild cognitive impairment (MCI) remains limited. We assessed whether compensating the circumpapillary retinal nerve fiber layer (cpRNFL) thickness for multiple demographic and anatomical factors as well as the combination of macular layers improves the detection of MCI and AD. Methods This cross-sectional study of 62 AD (n = 92 eyes), 108 MCI (n = 158 eyes), and 55 cognitively normal control (n = 86 eyes) participants. Macular ganglion cell complex (mGCC) thickness was extracted. Circumpapillary retinal nerve fiber layer (cpRNFL) measurement was compensated for several ocular factors. Thickness measurements and their corresponding areas under the receiver operating characteristic curves (AUCs) were compared between the groups. The main outcome measure was OCT thickness measurements. Results Participants with MCI/AD showed significantly thinner measured and compensated cpRNFL, mGCC, and altered retinal vessel density (p

The study proposed a method for determining total nitrogen (TN) content in activated sludge by ultrasound pre-treatment assisted wet method. Based on the single-factor experiment, with the TN content as the response value, the response surface methodology was employed to examine the individual and interactive effects of three factors: the dilution multiple of the sludge mixture, ultrasonic time, and ultrasonic power. At the same time, the physico-chemical parameters and the digestion–oxidation parameters were optimised. The results indicated that the optimal parameters were as follows; sludge dilution multiple of 225 times, stirring rate of 400 r/min, ultrasonic time of 22 minutes, ultrasonic power of 720 W, and optimal added volume of potassium persulfate at 8 mL with a digestion time of 40 minutes. The relative standard deviation (RSD) for the parallel determination of TN in sludge samples using ultrasonic pre-treatment assisted wet method was ≤2.77%, with a spike recovery rate of 98.49–101.43%. The method, ultrasonic pre-treatment assisted wet method to determine TN concentration in activated sludge, was simpler to operate, more accurate. [ABSTRACT FROM AUTHOR]

Graphitic carbon nitride (g‐C3N4) assisted photocatalytic production of hydrogen peroxide (H2O2 has already attracted the interest of many researchers due to its environmental sustainability. Nevertheless, the inherent drawbacks of g‐C3N4 limit its progress. Metal‐free modification strategies, including nanostructure design, defect introduction, doping, and heterojunction construction, have been developed to improve the efficiency of g‐C3N4 photocatalytic H2O2 production. Compared to metal modification, metal‐free strategies avoid the use of precious metals and the leaching of heavy metal ions, which have the advantages of good stability and environmental friendliness. However, a comprehensive review of H2O2 production from g‐C3N4 modified by metal‐free strategies is still lacking. This review first recaps the mechanism of photocatalytic H2O2 production by g‐C3N4, including photoexcitation, carrier separation and redox reactions. Then, the perspective advances in metal‐free modified g‐C3N4 photocatalysts are presented, with the special focus on the kernel connection between different strategies and mechanism based on the pivotal stages of H2O2 production. Subsequently, recent applications of g‐C3N4‐based photocatalysts for in situ generated H2O2, mainly including water purification and organic synthesis, are briefly discussed. Finally, the prospects of g‐C3N4‐based photocatalysts are envisioned with the hope that it will have "something to do" in the field of H2O2 production. [ABSTRACT FROM AUTHOR]

Objective To explore the application of FOCUS-PDCA nursing mode in intensive medical observation points for COVID-19 close contacts and the effect of preventing and controlling epidemic situation. Methods FOCUS-PDCA nursing work mode was used to analyze the risk of infection among nurses. Combined with modern artificial intelligence technology, the implementation process of PDCA cycle was replaced with the "non-contact" implementation process. Results The centralized medical observation point operated for 25 days, and a total of 24 close contacts were treated. The nursing work was carried out successfully without infection, and the nursing quality was continuously improved. Conclusion The “non-contact” nursing practise based on FOCUS-PDCA mode can produce certain satisfactory effects. (目的 探讨FOCUS-PDCA护理工作模式在新型冠状病毒肺炎密切接触者集中医学观察点的应用及防控疫情的效果。方法 采用FOCUS-PDCA护理工作模式分析护理人员感染的风险问题, 与现代人工智能技术相结合, 将PDCA循环的实施过程改进为“无接触式”实施过程。结果 集中医学观察点运行25 d, 共收治24例密切接触者, 护理工作顺利开展, 无感染事件发生, 持续提高护理质量。结论 采用基于FOCUS-PDCA 的“无接触”护理工作模式, 效果较好, 值得借鉴。)

In recent years, facial emotion recognition (FER) has been a popular topic in affective computing. However, FER still faces many challenges in automatic recognition for several reasons, including quality control of sample data, extraction of effective features, creation of models, and multi-feature fusion, which have not been thoroughly researched and therefore are still hot topics in computer visualization. In view of the mature development of deep learning, deep learning methods are increasingly being used in FER. However, because deep learning requires a large amount of data to achieve effective training, many studies have employed transfer learning to compensate for this drawback. Nevertheless, there has been no universal approach for transfer learning in FER. Accordingly, this study used the five classic models in FER (i.e., ResNet-50, Xception, EfficientNet-B0, Inception, and DenseNet121) to conduct a series of experiments: data preprocessing, training type, and the applicability of multi-stage pretraining. According to the results, class wight was the optimal technique for data balance. In addition, the freeze + fine-tuning training type can produce higher accuracy, regardless of the size of the dataset. Multi-stage training was also effective. Compared with the model accuracy in previous studies, the accuracy achieved in this study using the proposed transfer learning method was superior for both large and small datasets. Specifically, on AffectNet, the accuracy for the ResNet-50, Xception, EfficientNet-B0, Inception, and DenseNet-121 models increased by 8.37%, 10.45%, 10.45%, 8.55%, and 5.47%, respectively. On FER2013, the accuracy for these models increased by 5.72%, 2%, 10.45%, 5%, and 9%, respectively. These results proved the validity and advantages of the experiments in this study.

ObjectivesThis paper aims to study the fatigue damage problem of large ship prone to springing effect.MethodsThe test result in two generation 400 000 DWT very large ore carrier (VLOC) are compared based on ship model test method, and the relation of sea states and fatigue including hull girder springing are analyzed. According to the recursion method, a classical sea state is selected from a series of model test sea states.ResultsThe results show that the fatigue damage magnification effect has a trend relationship with significant wave height and encounter period. The concept of "dominant sea state" is proposed based on the recurrence method, and the expression of fatigue damage magnification factor is improved. The springing effect varies significantly according to different sea states, and the fatigue damage magnification effect of springing increases very rapidly as significant wave height and encounter period decrease.ConclusionsReplacing multiple model test loading conditions with dominant sea state could allow relatively accurate fatigue damage magnification factor to be ascertained rapidly. As such, this paper can provide references during the initial design period.

Abstract Keypoint‐based object detection is one of the most efficient and speedy methods at present, yet its performance is often worse than the anchor‐based method. Without prior settings in the keypoint‐based method, the huge search space of the keypoints results in the high recall but low precision. In this paper, the wide dual‐path backbone network is introduced as a feature extractor to extract richer original information, which has fewer parameters and better classification performance. Then, the attention fusion module is designed to effectively fuse the dual‐path with the consideration of the respective advantages of the residual‐path and the densely connected path. In order to provide more accurate pixel‐level information for keypoint prediction, the upsample dual‐attention module is proposed to recover the spatial size of the feature map, which integrates multi‐scale of channel‐wise and spatial attention. Compared with other state‐of‐the‐art detectors, this method has achieved accuracy‐efficiency results with fewer parameters, lower FLOPs, and smaller model size. Experimental results show that the proposed wide dual‐path backbone network has achieved 4.98% top1‐error on the CIFAR‐10 classification dataset. On the PASCAL VOC object detection dataset, this model has achieved an accuracy‐efficiency tradeoff result of 78.3% mAP at the speed of 41 FPS.

Proton exchange membrane fuel cells (PEMFCs) as energy conversion devices for hydrogen energy are crucial for achieving an eco-friendly society, but their cost and performance are still not satisfactory for large-scale commercialization. Multiple physical and chemical coupling processes occur simultaneously at different scales in PEMFCs. Hence, previous studies only focused on the optimization of different components in such a complex system separately. In addition, the traditional trial-and-error method is very inefficient for achieving the performance breakthrough goal. Machine learning (ML) is a tool from the data science field. Trained based on datasets built from experimental records or theoretical simulation models, ML models can mine patterns that are difficult to draw intuitively. ML models can greatly reduce the cost of experimental attempts by predicting the target output. Serving as surrogate models, the ML approach could also greatly reduce the computational cost of numerical simulations such as first-principle or multiphysics simulations. Related reports are currently trending, and ML has been proven able to speed up tasks in this field, such as predicting active electrocatalysts, optimizing membrane electrode assembly (MEA), designing efficient flow channels, and providing stack operation strategies. Therefore, this paper reviews the applications and contributions of ML aiming at optimizing PEMFC performance regarding its potential to bring a research paradigm revolution. In addition to introducing and summarizing information for newcomers who are interested in this emerging cross-cutting field, we also look forward to and propose several directions for future development.

Abstract Genetic co-expression network (GCN) analysis augments the understanding of breast cancer (BC). We aimed to propose GCN-based modeling for BC relapse-free survival (RFS) prediction and to discover novel biomarkers. We used GCN and Cox proportional hazard regression to create various prediction models using mRNA microarray of 920 tumors and conduct external validation using independent data of 1056 tumors. GCNs of 34 identified candidate genes were plotted in various sizes. Compared to the reference model, the genetic predictors selected from bigger GCNs composed better prediction models. The prediction accuracy and AUC of 3 ~ 15-year RFS are 71.0–81.4% and 74.6–78% respectively (rfm, ACC 63.2–65.5%, AUC 61.9–74.9%). The hazard ratios of risk scores of developing relapse ranged from 1.89 ~ 3.32 (p

The single-cycle transient high-concentration organic load shock test was conducted on the micropressure swirl reactor (MPSR) and sequencing batch reactor (SBR) to assess their capability to different levels of transient organic load shock (OLS), including pollutant removal, sludge characteristics, and microbial characteristics. The results indicated that the impact-resistance of biological treatment systems was related to reactor structure and microbial community distribution. When subjected to transient chemical oxygen demand (COD) load of 0.81, 1.28, and 1.70 g / (L · d a y) , respectively, both reactors exhibited effective organic removal capabilities. Compared with MPSR, within a certain shock range, nitrification in the SBR was less affected by shock and had a higher effect on nitrogen and phosphorus removal. However, when shock load reached 1.70 gCOD/(L·day) , the denitrification was sufficient in the MPSR, and microorganisms continued to secrete polysaccharides (PS), which was beneficial for adsorbing pollutants and resisting impacts. Under steady-state conditions, the SBR exhibited excellent nitrification with a relative abundance of 60.7% for nitrifying functional groups such as Thiothrix. Compared with the SBR, the MPSR displayed a higher microbial community diversity, with a variety of phosphorus-removing bacteria and denitrifying bacteria genera occupying aerobic and anoxic partitions within the same space, which was profitable for effective denitrification and phosphorus removal under high OLS. Besides, the enrichment of Flavobacterium and Zoogloea sp. at high concentrations significantly enhanced the MPSR's resistance to OLS. This study provides a theoretical basis for effective measures by different processes in response to organic load shocks. [ABSTRACT FROM AUTHOR]