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• ZIF-8 framework enhanced the inherent fluorescence of target DOX. • Ag/AuNCs@ZIF-8 was used for ratiometric fluorescence detection of DOX in foods. • The linear response range to DOX was 0.001–20 μg mL−1 with a LOD of 36.8 ng mL−1. • The recoveries for DOX in spiked foods achieved 90.3 %–100.5 % with RSD < 4.8 %. This study successfully encapsulated the Ag+-doped Au nanoclusters (Ag/AuNCs) within the ZIF-8 framework to construct a novel Ag/AuNCs@ZIF-8 ratiometric fluorescent probe for the antibiotic doxycycline (DOX) detection. The incorporation of Ag+ contributed to the fluorescence enhancement of the nanoclusters through the "silver effect", consequently improving the stability of the developed bimetallic Ag/AuNCs. Furthermore, the encapsulation of bimetallic Ag/AuNCs within the ZIF-8 framework restricted their intramolecular vibrations, resulting in further amplification of fluorescence intensity at 595 nm. The ZIF-8 also sensitized the restoration of DOX green fluorescence at 515 nm. Within the concentration range of 0.001–20 μg mL−1, the ratio of fluorescence intensity (F 515 / F 595) exhibited a favorable linearity for DOX concentration, with a detection limit of 36.8 ng mL−1. This ratiometric fluorescence approach had the promising potential for accurate and efficient quantitative detection of DOX residue in food and served as a valuable reference for rapid monitoring of food contaminants. [ABSTRACT FROM AUTHOR]

• Construct a general incomplete data processing model for the same structure incomplete data set. • Newly define the concept of abnormal degree. • Apply the Bisection method to the division of the missing rate intervals. • Newly proposed two judgment rules to ensure the feasibility of the processing method. • Apply to incomplete data processing in similar fields or with similar structures. Aiming at the problem of incomplete data such as outliers and missing values in machine learning, an incomplete data processing method based on the measurement of missing rate and abnormal degree was proposed in this paper. In this method, the outlier problem that are easy to be ignored was fully considered, and the Bisection method in the data structure field was applied to reduce the interval length and find the data distribution law. And then, an incomplete data processing model for static-structure or fixed-structure data set in a certain field or research direction was constructed. In the model establishment stage, the rules of the outlier processing part were first explored, including the boundary condition for outlier processing, and the relative applicable condition of using the direct-discarding method for outliers. Then, the rules of the missing value processing part were later explored, including the boundary condition for missing value processing, the relative applicable condition of using the direct-discarding method for missing values, the division of different missing rate intervals and the corresponding applicable numerical filling methods. In this research, the loose particle localization data set was taken as the test object, and the applicable incomplete data processing model was established. Specifically, the model can process outliers with the boundary condition of 23%, and the relative applicable condition of using the direct-discarding method for outliers is 2%. While the boundary condition that can process missing values is 67%, and the relative applicable condition of using the direct-discarding method for missing values is 3%. In the range of 3% to 67%, the range of missing rate that processing by statistical-filling methods is 3% to 7%, and the range of missing rate that processing by kNN prediction model is 7% to 67%. The applicable incomplete data processing model was verified on multiple loose particle localization data sets. Test results show that the prediction accuracy achieved by the classification learner based on the parameter-optimized Random Forest has significantly improved on the data set before and after processing, and the average improvement is 5.04%, which effectively proves the feasibility and practicability of the method proposed in this paper. Theoretically, it can be extended and applied to the processing of incomplete data sets in similar fields or with same structures, and has important reference significance for the research on incomplete data processing in machine learning. In addition, it has important practical value for improving the data completeness and data regularity of feature data sets in specific application fields. [ABSTRACT FROM AUTHOR]

The existing broiler health monitoring technology has problems such as low automation, unstable monitoring results, and low practical value, making it difficult to provide timely and reliable broiler health monitoring results. The broiler sound signal can provide feedback on their health. A widely validated and correct experience is to analyze the frequency of coughs in a segment of broiler sound signal to determine the health of the broiler group. Based on this, in this paper, the authors proposed a new broiler health monitoring technology based on sound detection. The broiler health monitoring problem is cleverly transformed into a multi-classification problem, which can be solved by identifying the sound types in broiler sound signals. Specifically, the audio signal collection system was designed to complete signal collection and preliminary signal filtering. Wiener filtering was used for deep signal filtering. The 60-dimensional sound features with good performance from three aspects, time-frequency domain, Mel-Frequency Cepstral Coefficients, and sparse representation were extracted, and a preliminary data set was created. Min-max normalization was used to align the numerical distribution of the data set, and a high-quality data set was created. Multi-classification models based on different classification algorithms and neural networks were trained, and the best-performing Random Forest was obtained, thus parameter optimization was carried out, and the optimal multi-classification model was obtained, achieving a classification accuracy of 91.14%. The visualization platform was built to process the classification results of the multi-classification model, completing majority voting processing and cough rate calculation, thereby achieving broiler health monitoring. In addition, the definitions of cough rate and prediction accuracy were newly proposed. A large number of experiments have verified the feasibility of the broiler health monitoring technology proposed in this paper, with an average prediction accuracy of 98.97% achieved. • Newly propose a complete broiler health monitoring technology based on sound detection. • Transform the broiler health monitoring problem into the sound type identification problem. • Newly propose an index of cough rate to evaluate the health of broiler groups. • Newly propose a data quality improvement scheme. • Obtain the highest prediction accuracy of broiler health monitoring in this field, currently. [ABSTRACT FROM AUTHOR]

CBM-IV mechanism is one of the most-commonly used atmospheric reaction mechanisms in the model studies of air pollution. In this study, by using a concentration sensitivity analysis, we investigated the dependence of NO , NO 2 and O 3 on the initial air composition in the CBM-IV mechanism under a typical urban condition. The initial values of the components that are identified important in the sensitivity analysis were also modified to see the relevant changes in the reaction system. The results show that at the beginning of the simulation, NO , NO 2 and O 3 are mostly affected by the titration reaction. During this period, the NO mixing ratio depends negatively on the initial O 3 and positively on the initial nitrogen oxides. Moreover, the NO 2 mixing ratio was found positively correlated with the initial NO , while its dependence on the initial O 3 transits between positive and negative. In contrast to that, O 3 at this stage has the strongest positive dependence on its own initial concentration, but a negative association with the initial NO. However, as the reaction proceeds, the dependence of O 3 and the nitrogen oxides on the initial air composition changes. During the end of the simulation, the change of NO and NO 2 was found moderately associated with their initial values, but the influence caused by the modification of the initial O 3 is negligible. Similarly, ozone at this time was also found less impacted by its initial value. Instead, the ozone level under equilibrium relies mostly on the initial NO and NO 2 . • The dependence of CBM-IV on the initial air composition is investigated. • At the early stage, NO x and ozone are determined by the titration reaction. • The dependence of the final ozone level on the initial value of ozone is weak. [ABSTRACT FROM AUTHOR]

The maximal energies of line graphs are determined over all graphs with fixed matching number, fractional matching number and bipartiteness, respectively. The extremal graphs are characterized. [ABSTRACT FROM AUTHOR]

Abstract The distribution patterns of equivalent static seismic lateral forces used in current structural design codes are mostly derived from elastic vibration modes. Such force patterns may not be consistent with actual conditions when structures behave into the inelastic range or even into the collapse stage. In this study, a new design method for multi-story buildings that uses an optimum lateral force pattern is proposed based on the concept of uniform damage distribution at the collapse state of these buildings. To reduce the computational cost of statistical analyses for deriving the optimum lateral force pattern, the non-linear story behaviors are represented by a story shear-deformation model with tri-linear relationships. Systematic parametric analyses are performed to study the influences of various ground motion parameters (site condition, magnitude, distance to rupture, and near-fault effect) and structural parameters (number of stories, fundamental period, damping ratio, post-yielding stiffness, ductility, and degrading stiffness) on the optimum lateral force pattern. Formulas for the optimum lateral force pattern that are applicable for practical seismic designs are provided. The collapse resistant capacities of structures designed using the proposed and conventional methods are compared by fragility analyses. Results show that the proposed method increases the seismic collapse resistant capacity of structures under the same construction cost. Highlights • Optimum story lateral force pattern for structural seismic design is given. • Influence parameters on the optimum story lateral force pattern are discussed. • New method is implemented within the current seismic design framework. • New method leads to larger collapse resistant capacity with same construction cost. [ABSTRACT FROM AUTHOR]

• Shift the research focus to the features in the localization data set. • Features in data set were processed with approximately the same scale. • Newly designed multi-channel weighted threshold feature selection method. • Achieve the highest plane and spatial localization accuracy in related field. • An important supplement to the existing loose particle localization technology. Existing localization technology on loose particles inside sealed electronic equipment focuses on the selection and parameter optimization of classification algorithms;the features in the specific region has not been considered. Targeting this problem, this paper shifts the research focus to features in the localization dataset and proposes a feature optimization method. First, for the missing values in the localization dataset, the numerical filling method using a variety of statistical functions or directly discarding them are compared to obtain the optimal missing value processing method. Second, different processing methods using z-score standardization, min–max standardization, and row normalization are compared to obtain the best standardization or normalization method. Third, statistic-based and model-based feature selection methods are sutdied and the Pearson correlation coefficient, p-value, and the feature importance of decision trees are used to quantitatively measure the feature performance. On this basis of this measurement, the multi-channel characteristics of the localization technology on loose particles are comprehensively considered, and a multi-channel weighted threshold feature selection method is designed. The feasibility of the new method is verified on the localization dataset. Finally, verification datasets, representing aerospace electronic modules and aerospace power supplies, are established to verify the practicability and universality of the feature optimization method. The results show that the plane and spatial localization accuracy achieved on the localization dataset increased from 87.01% and 83.67% to 95.23% and 95.51%, respectively, after feature optimization, and the plane and spatial localization accuracy achieved on the verification datasets increased from 84.51% and 80.64%% to 90.57% and 89.16%, respectively. This is the highest localization accuracy currently achieved in aerospace engineering. The current study is an important supplement to loose particle detection research and has important practical value for improving the reliability of aerospace systems. This method can also be applied to other localization research on fault sources and acoustic emission source based on machine learning. [ABSTRACT FROM AUTHOR]

• Solve the loose particle localization problem by applying classification algorithms. • Optimize the multi-channel signal synchronous acquisition circuit. • Design two signal preprocessing methods for pulse regularization. • Extract and select features that can reflect the locations of loose particles. • Obtain the localization model based on parameter-optimized Random Forest. Due to the complex internal structure and uneven composition material of sealed electronic equipment, traditional acoustic emission source localization methods are unsuitable for the research of locating loose particles inside sealed electronic equipment. Aiming at this problem, the author analyzed the essence of locating loose particles from another angle, considered converting the loose particle localization problem into the multi-classification problem, and sought for solving. Based on the existing loose particle detection system, the multi-channel signal synchronous acquisition circuit was designed to synchronously collect loose particle signals. The two-stage dual-threshold pulse extraction algorithm and the multi-channel pulse-matching algorithm were designed to preprocess the collected loose particle signals. Multiple features with good localization performance were extracted and selected from the processed loose particle signals, and the localization data set was eventually established. The localization performances of different classification algorithms were analyzed and compared by performing them on the localization data set, respectively, then the parameters of better-performed Random Forest were optimized. Finally, the loose particle localization model based on parameter-optimized Random Forest was used to perform localization tests on the aerospace power supply. Test results show that the average localization accuracy is 84.08%, which is the highest localization accuracy achieved in aerospace engineering area of the world currently. It is an important supplement to the loose particle detection research and has important application value for improving the reliability of aerospace system. Theoretically, it can be applied to the research of collision signal localization with similar generation mechanism. [ABSTRACT FROM AUTHOR]

• Introduce a new approach that using spectrograms for feature extraction. • Newly propose a complete route of feature optimization to construct a high-quality dataset. • Introduce a new fusion classification model achieving a very high prediction accuracy. • Contribute to the animal welfare and promote the development of broiler farming practices. This paper presents a sound recognition method for white feather broilers using spectrogram features and a fusion classification model, with the goal of achieving accurate classification of white feather broilers sound signals and providing a reliable basis for monitoring their health. In the training part, after five steps of sound signal acquisition, pre-processing, feature extraction, feature optimization, and model training, a fusion classification model with strong reliability is constructed for practical application scenarios. In the testing part, the method is applied to a real farming scenario of white feather broilers, and the stability of the multi-classification models and the reliability of the fusion classification model are verified. The fusion classification model comprises Random Forest, K-nearest neighbor, and RBF-based SVM. Results from multiple tests showed that the highest classification accuracies achieved by the three multi-classification models were 100%, 86.67%, and 93.33%, respectively. The average prediction accuracy of the fusion classification model on multiple audio signals was 98.57%, the results effectively demonstrate the feasibility and practicality of the proposed method. [ABSTRACT FROM AUTHOR]

The implementation of different chemical mechanisms in air quality models can exert a significant impact on simulations of ozone and its precursors in the troposphere. In the present study, we applied three different chemical mechanisms (RADM2, SAPRC99 and CBMZ) in a three-dimensional mesoscale air quality model, WRF-Chem, to capture the spatiotemporal variation of the surface ozone and its precursors such as NO x and VOCs in Shanghai, China in May 2016, so that the influences on simulations exerted by using different chemical mechanisms can be clarified. Observational data from surface monitoring stations were also adopted for the comparison of the model simulations. By comparing the model predictions with the observational data, we found that the model with different chemical mechanisms can generally capture the temporal change of ozone in Shanghai during the simulated time period. However, it was found that SAPRC99 predicts a higher ozone than the other two mechanisms during a high-ozone period, while RADM2 gives a higher ozone level in other time periods. In the present study, CBMZ gives a closer ozone prediction to observations than the other two mechanisms. Regarding the nitrogen related species, in simulations using RADM2 and SAPRC99, NO 2 is overestimated. In contrast, NO 2 predicted by CBMZ is closer to the observational data. A comparison between simulations using different mechanisms also shows that SAPRC99 predicts higher OH, HO 2 and NO x than the other two mechanisms. In contrast, RADM2 predicts the highest HNO 3 and ozone among these three mechanisms. In addition, simulations using different mechanisms all indicate that Shanghai city is VOC-controlled. However, VOCs and NO x concentrations predicted by CBMZ were found to be mostly lower than those predicted by the other two mechanisms under a same condition. • We investigated the impacts on simulations of ozone and its precursors in Shanghai, China by using three different mechanisms. • The WRF-Chem model instrumented with different mechanisms performs differently in simulating air constituents. • Reasons for the different predictions of these mechanisms were discussed. [ABSTRACT FROM AUTHOR]

Simulations of the spatiotemporal distribution of air constituents such as ozone and NO 2 on the Tibetan Plateau (TP) can be affected by many factors such as the altitude of the observational site. In the present study, we used a mesoscale meteorological model coupled with chemistry (WRF-Chem) with two different anthropogenic emission inventories (MEIC and MEIC + EDGAR), to investigate the association of the simulation accuracy of ozone and NO 2 on TP with the altitude of the observational sites. Statistical parameters such as the correlation coefficient (r) and the index of agreement (IOA) were also calculated to evaluate the model performance in capturing the concentration change of these two air constituents (i.e., ozone and NO 2). The results revealed that when only the MEIC emission inventory is implemented, the deviation between the model simulations and the observations becomes larger with the increase of the altitude of the city. Moreover, it was also found that when the EDGAR emission inventory was added into the model (i.e., MEIC + EDGAR scenario), the negative dependence of the simulation accuracy of ozone on the altitude becomes weaker than that using only MEIC inventory, due to the change in ozone simulations in high-altitude cities. In contrast, simulation results of NO 2 using two different emission inventories were found to be similar to each other. • The accuracy of ozone and NO 2 simulations on Tibetan Plateau depends negatively on the altitude of the cities. • The simulated NO 2 in border areas of Tibetan Plateau is only slightly affected by emissions from regions outside China. • Observational data from 14 cities on TP were used to assess the model behavior. [ABSTRACT FROM AUTHOR]