Application of hybrid multi-criteria decision-making approach to analyze wastewater microalgae culture systems for bioenergy production.

Bioenergy generation from microalgae can significantly contribute to climate mitigation and renewable energy production. In this regard, several multi-criteria decision-making method were employed to prioritize appropriate microalgae culture system for bioenergy production. Entropy weight, Criteria Importance Through Intercriteria Correlation (CRITIC) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) were the employed MCDA method. Fourteen microalgae culture systems were selected as a case study, which contain teen monoculture and four dual-culture. Initially, through ans in-depth review of the literature and expert views, four categories total eight indicators were selected as the evaluation indices of the study, namely 1) Proliferation: Half growth cycle and Max growth rate,2) Biomass output: Bio-crude yield and Lipid yield, 3) Nutrient utilization: residual concentration of total Nitrogen and total Phosphorus, and, 4) Stability: coefficient of variation of Bio-crude yield and Lipid yield. The result indicated that "Pediastrum sp. & Micractinium sp." was identified as the most bioenergy potential microalgae culture system, and the evaluation results of entropy weight method and CRITIC method are similar. It is pertinent to note that 1)the entropy weight method exhibits lower sample size requirements, 2) the critic method excels when dealing with larger sample sizes, and 3) the TOPSIS method necessitates the incorporation of appropriate weighting methods to ensure credible results. In the application stage, the key indicators related to cost can be further included in the evaluation indices.

Research on ecological risk assessment and risk level prediction in the central urban area of Chongqing, China.

Complex geological conditions, coupled with urban expansion, resource consumption, and rapid economic development, make the ecological environment of Chongqing's central urban area more vulnerable. To enhance the carrying capacity of resources and the environment in this region, it is significant to scientifically assess the trend of ecological risk changes in Chongqing. The article developed an ecological risk assessment index system for Chongqing, utilizing the "pressure-state-response" framework. The entropy weight method (EWM) is employed to assign weights to each variable, subsequently establishing a grey weighted clustering evaluation model (GWCEM). We evaluated the ecological risks of nine central urban areas in Chongqing from 2005 to 2021 and projected the ecological risk levels and changes from 2022 to 2025. Our research indicates that the comprehensive ranking of influencing factors of ecological risk in Chongqing follows this order: response factor > pressure factor > state factor. Throughout the study period, we observed a decrease in the ecological risk values of Ba'nan, Shapingba, Jiulongpo, Nan'an and Yubei Districts by more than 50%. These decline rates are accelerating and regional differences in ecological risk levels are diminishing. From 2022 to 2025, except Shapingba, Jiangbei, Yuzhong, and Nan'an District which consistently maintained a "low-risk" level, the ecological risk levels of all other areas continue to decrease, aligning with a "low-risk" classification by 2025. Based on the results of ecological risk assessment and ecological risk level prediction, corresponding recommendations are proposed for ecological environment protection and ecological risk management in the central urban area of Chongqing.

Bilateral Matching Method for Business Resources Based on Synergy Effects and Incomplete Data.

On the third-party cloud platform, to help enterprises accurately obtain high-quality and valuable business resources from the massive information resources, a bilateral matching method for business resources, based on synergy effects and incomplete data, is proposed. The method first utilizes a k-nearest neighbor imputation algorithm, based on comprehensive similarity, to fill in missing values. Then, it constructs a satisfaction evaluation index system for business resource suppliers and demanders, and the weights of the satisfaction evaluation indices are determined, based on the fuzzy analytic hierarchy process (FAHP) and the entropy weighting method (EWM). On this basis, a bilateral matching model is constructed with the objectives of maximizing the satisfaction of both the supplier and the demander, as well as achieving the synergy effect. Finally, the model is solved using the linear weighting method to obtain the most satisfactory business resources for both supply and demand. The effectiveness of the method is verified through a practical application and comparative experiments.

A study on the analysis of dynamical transmission behavior and mining key monitoring stations in PM and O3 networks in the Beijing-Tianjin-Hebei region of China.

To investigate the dynamical transmission behavior of pollutants and explore the roles played by monitoring stations in regional air pollutants transportation, we constructed a new model for the dynamical transmission index by adopting a statistics model that employs complex network analysis along with terrain data, meteorological variables, and air quality data. The study is conducted in Beijing-Tianjin-Hebei region with 70 stations in 13 cities. The findings indicated that the regional dynamical transmission networks were characterized by the participation of 67 out of 70 stations, as determined by node number. Among the model characteristics, the average path length and the average clustering coefficient, within the ranges of 2.08-2.32 and 0.26-0.51, respectively, maintained reasonable small-world characteristic. For the seasonal transmission features, the networks for PM2.5, PM10 in winter, and O3 in summer shared similar modeling characteristics with those of yearly networks. This suggested that the networks for these two seasons could represent the yearly transmission features. By employing the entropy weight method, the key monitoring stations numbered 1011 A, 1026 A, and 1010 A, which are located in Tianjin, Shijiazhuang, and Beijing, exerted significant impacts on air pollution transmission path in cities. The novel model has demonstrated its soundness and effectiveness in terms of capturing the behavior of transmission as well as the distinguishing roles of these crucial monitoring stations. This methodology could be employed for the construction of additional monitoring stations, identification of possible pollution sources, and prioritization of key pollution areas, thus providing valuable insights for environmental protection and management.

AODV-EOCW: An Energy-Optimized Combined Weighting AODV Protocol for Mobile Ad Hoc Networks.

The Ad Hoc On-demand Distance Vector (AODV) is a routing protocol for mobile ad hoc networks (MANETs) and other wireless ad hoc networks. The vanilla AODV protocol is simple and easy to implement because it only uses the hop count as a routing metric. Single-metric route determination also causes problems, such as network congestion and energy exhaustion, which limit the usage of AODV in resource-limited applications. To solve these problems, the authors propose a new routing protocol that combines the analytic hierarchy process (AHP), the entropy weight method (EWM), and AODV. The proposed protocol uses energy, congestion, and the hop count as metrics and weights these three metrics using AHP and EWM. To address the importance of energy in applications, such as drones, the proposed protocol chooses different comparison matrices for AHP at different node residual energy levels. Finally, the node chooses the best route link according to the score (sum of weighted metrics). It is also suitable for wireless sensor networks because the proposed protocol considers the residual energy of the node. The simulation results show that the improved routing protocol can effectively reduce the average end-to-end delay and energy consumption and prolong the lifetime of the whole network.

Monitoring of surface deformation in mining area integrating SBAS InSAR and Logistic Function.

SBAS InSAR has long been used to monitor the mining surface deformation, and its research has been of great interest to researchers worldwide. For the unsatisfactory accuracy of SBAS InSAR-monitored mining surface deformation results, a new corrected model is proposed by integrating SBAS InSAR and Logistic Function. Firstly, the time series deformation results of the mining area were obtained by SBAS InSAR, and the variation law of the differences between SBAS InSAR- and leveling-monitored deformation values was statistically analyzed. Subsequently, the corrected model was constructed using the logistic linear regression analysis function and solved using the Levenberg-Marquardt algorithm. Finally, the corrected high-precision time series deformation results in the mining area were obtained. A mining area in Shandong Province of China was taken as the research object, and the practical application effect of the proposed corrected model was verified. Results showed that the Logistic Function could describe the variation law of the differences relatively accurately, and the corrected results were significantly better than the SBAS InSAR-monitored results, and the RMSEs of the corrected results were improved by 33-58%. The accuracy of SBAS InSAR-monitored mining surface deformation was effectively improved.

Improving assessment quality of soil natural attenuation capacity at the point and regional scales.

Soil natural attenuation capacity (NAC) is an important ecosystem service that maintains a clean environment for organisms in the soil, which in turn supports other services. However, spatially varying indicator weights were rarely considered in the traditionally-used soil NAC assessment model (e.g., ecosystem-service performance model) at the point scale. Moreover, in the spatial simulation of soil NAC, the traditionally-used geostatistical models were usually susceptible to spatial outliers and ignored valuable auxiliary information (e.g., land-use types). This study first proposed a novel soil NAC assessment method based on the ecosystem-service performance model and moving window-entropy weight method (MW-EW) (NACMW-EW). Next, NACMW-EW was used to assess soil NAC in a typical area in Guixi City, China, and further compared with the traditionally-used NACtra and NACEW. Then, robust sequential Gaussian simulation with land-use types (RSGS-LU) was established for the spatial simulation of NACMW-EW and compared with the traditionally-used SGS, SGS-LU, and RSGS. Last, soil NAC's spatial uncertainty was evaluated based on the 1000 realizations generated by RSGS-LU. The results showed that: (i) MW-EW effectively revealed the spatially varying indicator weights but EW couldn't; (ii) NACMW-EW obtained more reasonable results than NACtra and NACEW; (iii) RSGS-LU (RMSE = 0.118) generated higher spatial simulation accuracy than SGS-LU (RMSE = 0.123), RSGS (RMSE = 0.132), and SGS (RMSE = 0.135); and (iv) the relatively high (P[NACMW-EW(u) > 0.57] ≥ 0.95) and low (P[NACMW-EW(u) > 0.57] ≤ 0.05) threshold-exceeding probability areas were mainly located in the south and east of the study area, respectively. It is concluded that the proposed methods were effective tools for soil NAC assessment at the point and regional scales, and the results provided accurate spatial decision support for soil ecosystem service management.

[Identification of quality markers of Gei Herba based on analytic hierarchy process-entropy weight method and network pharmacology].

Analytic hierarchy process(AHP)-entropy weight method(EWM) and network pharmacology were employed to identify the potential quality markers(Q-markers) of Gei Herba. According to the new concept of Q-markers in traditional Chinese medicine(TCM), the AHP-EWM was applied to quantitatively identify the Q-markers of Gei Herba. The AHP was used for the weight analysis of primary indicators(factor layer), and the EWM for the analysis of literature and experimental data of secondary indicators(control layer). In addition, network pharmacology was employed to build the "component-target-disease-efficacy" network for Gei Herba, and the components showing strong associations with the Qi-replenishing, spleen-invigorating, blood-tonifying, Yin-nourishing, lung-moistening, and phlegm-resolving effects of Gei Herba were screened out. According to the results of AHP-EWM and network pharmacology, four components, i.e., ellagic acid, gallic acid, gemin G, and gemin C, were finally identified as potential Q-markers of Gei Herba. In this study, the AHP-EWM and network pharmacology were employed to screen the Q-markers of Gei Herba, which provided ideas for the quantitative evaluation and identification of Q-markers of TCM.

[Effects of different drying methods on quality of male flowers of Eucommia ulmoides based on color and chemical composition].

To study the effects of different drying methods on the quality of male flowers of Eucommia ulmoides(MFOEU), we treated fresh MFOEU samples with drying in the shade(DS), vacuum freeze drying(VFD), high-or low-temperature hot air drying(HTHAD, LTHAD), microwave drying(MD), and vacuum drying(VD), respectively. The color, total flavonoid content, total polysaccharide content, and main active components such as geniposide, geniposidic acid, rutin, chlorogenic acid, galuteolin, pinoresinol diglucoside, and aucubin in MFOEU were taken as the evaluation indicators. The quality of MFOEU was comprehensively evaluated by entropy weight method combined with color index method, partial least squares discriminant analysis and content clustering heat map. The experimental results showed that VFD and DS basically kept the original color of MFOEU. The MFOEU treated with MD had higher content of total polysaccharides, phenylpropanoids, lignans, and iridoids. The MFOEU treated with LTHAD had higher content of total flavonoids and that treated with VD had lower content of active components. According to the results of comprehensive evaluation, the quality of MFOEU dried with different methods followed the order of MD>HTHAD>VFD>LTHAD>DS>VD. Considering the color of MFOEU, the suitable drying methods were DS and VFD. Considering the color, active components, and economic benefits of MFOEU, MD was the suitable drying method. The results of this study are of a reference value for the determination of suitable methods for MFOEU processing in the producing areas.

[Optimization of extraction process for classic prescription Yihuang Decoction based on Box-Behnken design-response surface methodology, standard relation, and analytic hierarchy process combined with entropy weight method].

Based on the concept of quality by design(QbD), the Box-Behnken design-response surface methodology combined with standard relation(SR) and analytic hierarchy process(AHP)-entropy weight method(EWM) was applied to optimize the extraction process of the classic prescription Yihuang Decoction. The content of geniposidic acid, phellodendrine hydrochloride, and berberine hydrochloride in Yihuang Decoction, the extract yield, and fingerprint similarity were used as the critical quality attributes(CQAs) of the extraction process. The extraction time, water addition, and extraction times were used as the critical process parameters(CPPs). After determining the levels of each factor and level through single-factor experiments, response surface experiments were designed according to the Box-Behnken principle, and the experimental results were analyzed. The SR between each sample and the reference sample under various evaluation indicators of different extraction parameters was calculated. The weights of the five evaluation indicators were determined using AHP-EWM, followed by comprehensive evaluation. A function model between CPPs and CQAs characterized by comprehensive scores was established to predict the optimal extraction process parameters. In the final comprehensive weight coefficients, the yield rate accounted for 43.1%, and the content of berberine hydrochloride, phellodendrine hydrochloride, and geniposidic acid accounted for 35.1%, 6.3%, and 15.5%, respectively. After comprehensive score analysis with SR, the established second-order polynomial model was statistically significant(P<0.01, and the lack of fit was not significant). The predicted optimal extraction conditions for Yihuang Decoction were determined as follows: 8-fold volume of water, extraction time of 1.5 h, and extraction once. The mean comprehensive score of the validation experiment was 85.77, with an RSD of 0.99%, and it met the quality control stan-dards for the reference sample of Yihuang Decoction. The results indicate that the optimized extraction process for Yihuang Decoction is stable and reliable, and the water extract is close in quality attributes to the reference sample. This can serve as a foundation for the research and development of granules in the future. Box-Behnken design-response surface methodology combined with SR and AHP-EWM can provide references for the modern extraction process research of other classic prescriptions.

Security Risk Intelligent Assessment of Power Distribution Internet of Things via Entropy-Weight Method and Cloud Model.

The current power distribution Internet of Things (PDIoT) lacks security protection terminals and techniques. Network security has a large exposure surface that can be attacked from multiple paths. In addition, there are many network security vulnerabilities and weak security protection capabilities of power distribution Internet of Things terminals. Therefore, it is crucial to conduct a scientific assessment of the security of PDIoT. However, traditional security assessment methods are relatively subjective and ambiguous. To address the problems, we propose to use the entropy-weight method and cloud model theory to assess the security risk of the PDIoT. We first analyze the factors of security risks in PDIoT systems and establish a three-layer PDIoT security evaluation index system, including a perception layer, network layer, and application layer. The index system has three first-level indicators and sixteen second-level indicators. Then, the entropy-weight method is used to optimize the weight of each index. Additionally, the cloud model theory is employed to calculate the affiliation degree and eigenvalue of each evaluation index. Based on a comprehensive analysis of all evaluation indexes, we can achieve the security level of PDIoT. Taking the PDIoT of Meizhou Power Supply Bureau of Guangdong Power Grid as an example for empirical testing, the experimental results show that the evaluation results are consistent with the actual situation, which proves that the proposed method is effective and feasible.

Multi-scenario simulation of ecological risk assessment based on ecosystem service values in the Beijing-Tianjin-Hebei region.

In this study, a framework for ecological risk assessment based on ecosystem service values and risk probability was established. Remote sensing was used to estimate the value of ecosystem services at the regional scale. Considering the natural and anthropogenic factors and using the entropy weight method to assign weights, probability index was constructed. In addition, multiple scenarios based on the ordered weighted averaging (OWA) method were simulated to reduce subjective uncertainty in the assessment. The results showed that the ecosystem service values generated by the gas regulation value accounted for the largest proportion, with a ratio of 46% in the Beijing-Tianjin-Hebei region. From 2005 to 2015, the value of ecosystem services decreased, falling by 2.5 × 107 Yuan. The level of ecological risk was relatively high, with a corresponding area ratio of 32.89%. Spatially, the areas with high risk were concentrated in the southeastern areas, and areas with relatively low risk were distributed in the western and northern areas. This high risk was probably caused by urbanization which was characterized by reduction of farmland and increase in impervious surface. Multi-scenario simulation showed that the areas of unstable ecological risk zones covered 30% and were mainly concentrated in the surroundings of developing cities. In areas of unstable risk distribution, the relationship between development and protection should be considered. This framework increases the reliability and practicability of ecological risk assessment results and has potential application value for regional risk control in the context of urbanization.

Entropy-Weight-Method-Based Integrated Models for Short-Term Intersection Traffic Flow Prediction.

Three different types of entropy weight methods (EWMs), i.e., EWM-A, EWM-B, and EWM-C, have been used by previous studies for integrating prediction models. These three methods use very different ideas on determining the weights of individual models for integration. To evaluate the performances of these three EWMs, this study applied them to developing integrated short-term traffic flow prediction models for signalized intersections. At first, two individual models, i.e., a k-nearest neighbors (KNN)-algorithm-based model and a neural-network-based model (Elman), were developed as individual models to be integrated using EWMs. These two models were selected because they have been widely used for traffic flow prediction and have been approved to be able to achieve good performance. After that, three integrated models were developed by using the three different types of EWMs. The performances of the three integrated models, as well as the individual KNN and Elman models, were compared. We found that the traffic flow predicted with the EWM-C model is the most accurate prediction for most of the days. Based on the model evaluation results, the advantages of using the EWM-C method were deliberated and the problems with the EWM-A and EWM-B methods were also discussed.

Spatial-Temporal Sensitivity Analysis of Flood Control Capability in China Based on MADM-GIS Model.

To facilitate better implementation of flood control and risk mitigation strategies, a model for evaluating the flood defense capability of China is proposed in this study. First, nine indicators such as slope and precipitation intensity are extracted from four aspects: objective inclusiveness, subjective prevention, etc. Secondly, the entropy weight method in the multi-attribute decision making (MADM) model and the improved three-dimensional technique for order preference by similarity to ideal solution (3D-TOPSIS) method were combined to construct a flood defense capacity index evaluation system. Finally, the receiver operating characteristic (ROC) curve and the Taylor plot method were innovatively used to test the model and indicators. The results show that nationwide, there is fine flood defense performance in Shandong, Jiangsu and room for improvement in Guangxi, Chongqing, Tibet and Qinghai. The good representativity of nine indicators selected by the model was verified by the Taylor plot. Simultaneously, the ROC calculated area under the curve (AUC) was 70%, which proved the good problem-solving ability of the MADM-GIS model. An accurate assessment of the sensitivity of flood control capacity in China was achieved, and it is suitable for situations where data is scarce or discontinuous. It provided scientific reference value for the planning and implementation of China's flood defense and disaster reduction projects and emergency safety strategies.

Security Risk Assessment Approach for Distribution Network Cyber Physical Systems Considering Cyber Attack Vulnerabilities.

With the increasing digitalization and informatization of distribution network systems, distribution networks have gradually developed into distribution network cyber physical systems (CPS) which are deeply integrated with traditional power systems and cyber systems. However, at the same time, the network risk problems that the cyber systems face have also increased. Considering the possible cyber attack vulnerabilities in the distribution network CPS, a dynamic Bayesian network approach is proposed in this paper to quantitatively assess the security risk of the distribution network CPS. First, the Bayesian network model is constructed based on the structure of the distribution network and common vulnerability scoring system (CVSS). Second, a combination of the fuzzy analytic hierarchy process (FAHP) and entropy weight method is used to correct the selectivity of the attacker to strike the target when cyber attack vulnerabilities occur, and then after considering the defense resources of the system, the risk probability of the target nodes is obtained. Finally, the node loads and node risk rates are used to quantitatively assess the risk values that are applied to determine the risk level of the distribution network CPS, so that defense strategies can be given in advance to counter the adverse effects of cyber attack vulnerabilities.

Will multi-industry supply chains' resilience under the impact of COVID-19 pandemic be different? A perspective from China's highway freight transport.

The pandemic caused by coronavirus disease 2019(COVID-19) continues to disrupt the global supply chain system, bringing new risks and challenges. The uncertainty created by COVID-19 makes it is difficult for various industries to deal with the pandemic. Since the pandemic, the supply chain's resilience has been discussed and examined in some studies. However, most existing works start from a single industry perspective or pay more attention to the disturbance caused by changes in the production side. Supply chain networks of different industries, mainly transport networks, are relatively limited under the epidemic's impact. In this paper, from the perspective of highway freight transport, a comprehensive competitiveness evaluation framework was proposed to reveal and the disruption and resilience of the supply chain under the outbreak based on nine indexes with five dimensions, including efficiency, capacity, activity, connectivity, and negotiability. Based on the availability of the data(Large-scale truck trajectory), we sorted out seven categories of Chinese industries(related to highway transport) and divided them into four categories respectively: (a) Slight disruption and worse resilience; (b) Slight disruption and remarkable resilience; (c) Serious disruption and worse resilience; (d) Serious disruption and remarkable resilience. The measurement results of supply chain network performance show that the industries (cold-chain, general products, and other industries) dominated by "Efficiency - Negotiability - Connectivity" are slightly disrupted (about 33%), forming a spatial diffusion with Wuhan(the city where the pandemic first broke out) as the disrupted center, spreading outward in a circle structure. Simultaneously, five urban agglomerations surrounding it have been impacted. By contrast, due to the strict isolation measures, the industries (building materials, construction, engineering, and high-value products industry) more vulnerable to be disrupted seriously (about 82%) tend to be the pattern of "Capacity - Activity". However, a large-scale centralized disruption was observed in the Triangle of Central China urban agglomeration was presented, resulting in almost stagnation of industry development. Meanwhile, as the future of the pandemic remains uncertain, the supply chain represented by the engineering industry, construction industry, etc are deserved to be paid more attention in line with they are prone to large-scale centralized damage due to the disruption of a single city node.

How does COVID-19 pandemic impact cities' logistics performance? An evidence from China's highway freight transport.

The pandemic COVID-19 which has spread over the world in early 2020 has caused significant impacts not only on health and life, but also on production activities and freight work. However, few studies were about the effect of COVID-19 on the performance of cities' logistics. Hence, this study focuses on the Belt and Road Initiative (BRI) and compares the changes in logistics performance from a spatial perspective caused by COVID-19 that are reflected on the highway freight between its 18 node cities in 2019 and 2020 of the same periods for 72 days. This study uses the entropy weight method to reflect the impact that COVID-19 has caused to the logistics level. Based on the modified gravity model, the impact on the logistics spatial connection between node cities was analyzed. These two aspects have been combined to analyze the logistics performance. The results show that the node cities have been affected by COVID-19 dissimilarly, and the impact has regional characteristics. The logistics level and spatial connection of Wuhan are the most seriously declined. The decline in logistics level has the same spatial variation law as the confirmed cases. The logistics connection between Wuhan and the surrounding node cities and the three-node cities in the northeast of China are also severely affected by the pandemic because of the expressway control policies. The regional distribution of logistics performance has differences, and the correlation of the logistics level and logistics spatial connection decreases. Besides, this study puts forward different recovery suggestions and policies for different belts in the BRI, such as focusing on restoring areas and giving full play to the role of the Chengdu-Chongqing urban agglomeration and logistics corridor. Finally, further provides corresponding suggestions for reducing the impact of emergencies from the perspectives of logistics hubs.

[Research on Q-markers of Polygoni Perfoliati Herba based on analytic hierarchy process-entropy weight method and fingerprints].

The potential quality markers(Q-markers) of Polygoni Perfoliati Herba were studied based on analytic hierarchy process(AHP)-entropy weight method(EWM), network pharmacology, and spectrum-effect relationship analysis. The AHP-EWM was used for quantitative identification of the Q-markers. To be specific, AHP was applied for the weight analysis of the validity, testability, and specificity of the first-level indexes, and EWM for the analysis of the second-level indexes supported by literature and experimental data. Based on literature and network pharmacology, the validity analysis was to study the component-target-disease-efficacy network, and select the components with the strongest correlation with the efficacy of clearing heat and removing toxin, diuresis and alleviating edema, and relieving cough. For the testability analysis, the high performance liquid chromatography(HPLC) and literature research were used to determine the 10 components in Polygoni Perfoliati Herba, and the fingerprints of Polygoni Perfoliati Herba were established at the same time. The specificity analysis was based on the statistics of the number of plants in which the components existed. Thereby, the 11 compounds: quercetin, oleanolic acid, ellagic acid, gallic acid, kaempferol, rutin, esculetin, quercetin-3-O-glucuronide, ursolic acid, protocatechuic acid, and ferulic acid, were identified as potential Q-markers. The 11 compounds were identified to have high anti-inflammatory activity, indicating that the 11 Q-markers may be the functional material basis. The result in this study is expected to serve as a reference for the quality control of Polygoni Perfoliati Herba.

Bearing Fault Feature Extraction and Fault Diagnosis Method Based on Feature Fusion.

Bearing is one of the most important parts of rotating machinery with high failure rate, and its working state directly affects the performance of the entire equipment. Hence, it is of great significance to diagnose bearing faults, which can contribute to guaranteeing running stability and maintenance, thus promoting production efficiency and economic benefits. Usually, the bearing fault features are difficult to extract effectively, which results in low diagnosis performance. To solve the problem, this paper proposes a bearing fault feature extraction method and it establishes a bearing fault diagnosis method that is based on feature fusion. The basic idea of the method is as follows: firstly, the time-frequency feature of the bearing signal is extracted through Wavelet Packet Transform (WPT) to form the time-frequency characteristic matrix of the signal; secondly, the Multi-Weight Singular Value Decomposition (MWSVD) is constructed by singular value contribution rate and entropy weight. The features of the time-frequency feature matrix obtained by WPT are further extracted, and the features that are sensitive to fault in the time-frequency feature matrix are retained while the insensitive features are removed; finally, the extracted feature matrix is used as the input of the Support Vector Machine (SVM) classifier for bearing fault diagnosis. The proposed method is validated by data sets from the time-varying bearing data from the University of Ottawa and Case Western Reserve University Bearing Data Center. The results show that the algorithm can effectively diagnose the bearing under the steady-state and unsteady state. This paper proposes that the algorithm has better fault diagnosis capabilities and feature extraction capabilities when compared with methods that aree based on traditional feature technology.

[Research on Q-markers of Eupatorium lindleyanum based on analytic hierarchy process-entropy weight method and network pharmacology].

The potential quality markers( Q-markers) of Eupatorium lindleyanum were studied with analytic hierarchy process(AHP)-entropy weight method(EWM) and network pharmacological method. Based on the concept of Q-markers of traditional Chinese medicine, AHP-EWM was employed to quantitatively identify the Q-markers of E. lindleyanum. AHP method was applied to the weight analysis of the validity, testability, and specificity of the first-level indexes, and EWM method was used to analyze the secondlevel indexes supported by literature and experimental data. At the same time, based on the theory and method of network pharmacology, the component-target-disease-efficacy network of E. lindleyanum was built, and the components most closely related to the efficacy of resolving phlegm and relieving cough and asthma were screened out. Through the integrated analysis of the results obtained with AHP-EWM and network pharmacological method, 13 compounds including rutin, quercetin, nepetin, cirsiliol, luteolin, hyperoside,isoquercitrin, kaempferol, caffeic acid, eupalinolide K, eupalinolide A, eupalinolide B, and eupalinolide C were comprehensively identified as the potential Q-markers of E. lindleyanum. The results provide a basis for the quality control of E. lindleyanum.