Dataset Information

A Comprehensive Operation Status Evaluation Method for Mining XLPE Cables.

ABSTRACT: At present, the online insulation monitoring and fault diagnosis of mining cables are extensively discussed, while their operation status assessment has not been deeply studied. Considering that mining cables are closely related to the safe and stable operation of coal mine power supply systems, a comprehensive evaluation method including the Analytic Hierarchy Process (AHP), the membership cloud theory, and the D-S evidence theory is proposed in this paper in order to accurately assess the operation status of the mining XLPE cable. Firstly, the membership cloud is introduced to solve the index membership degree and the weights are calculated by an improved weight vector calculation method. Secondly, the conversion from the base layer indicator membership degree to the target layer trust degree is realized based on the D-S evidence theory. Then, the cable operation status is judged via the trust degree maximum and the distribution of conflict coefficients is further analyzed to warn the indicators with a bad status in the base layer. Finally, the feasibility of the proposed evaluation method is verified by a sufficient and detailed case analysis.

SUBMITTER: Wang Y

PROVIDER: S-EPMC9571771 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature

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A Comprehensive Operation Status Evaluation Method for Mining XLPE Cables.

Wang Yanwen Y Chen Peng P Sun Yanying Y Feng Chen C

Sensors (Basel, Switzerland) 20220921 19

At present, the online insulation monitoring and fault diagnosis of mining cables are extensively discussed, while their operation status assessment has not been deeply studied. Considering that mining cables are closely related to the safe and stable operation of coal mine power supply systems, a comprehensive evaluation method including the Analytic Hierarchy Process (AHP), the membership cloud theory, and the D-S evidence theory is proposed in this paper in order to accurately assess the oper ...[more]

PMID: 36236275

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Project description:The development of the green economy has significantly impact the traditional mining industry. Mining enterprises must invest in green technology to reduce the environmental pollution caused by flying dust and soil erosion and are subject to increased scrutiny to be socially responsible when conducting their business. To address this issue, we consider a competitive mining supply chain system consisting of two excavators and two exclusive retailers. Among them, the excavators have a certain sense of corporate social responsibility (CSR), that is, in addition to pursuing economic profits, they also consciously pay attention to the interests of consumers. We establish three different game models that two excavators exhibit no CSR behaviour (NN), two excavators exhibit CSR behaviour (SS) and one excavator exhibits CSR behaviour (SN). We examine the optimal decision-making strategies and analyse the impact of social responsibility. Analytical results show that the optimal strategies of mining supply chain are different under different supply chain structures. The optimal decisions of the mining supply chain members are the same in each case under the NN and SS models. In the SN model, the optimal decision strategy value of mining supply chain members is always greater than non-socially responsible supply chain members. In SS model, when the intensity of social responsibility competition is low, two excavators reduce the wholesale price, and retailers reduce the sales price; when the intensity of social responsibility competition is strong, two excavators will increase the wholesale price, and retailers will increase the sales price. These help to promote product sales and increase the profits of the supply chain system. In SN model, with the increase of social responsibility competition intensity, the wholesale price of two excavators and the sales price of retailers first increased and then decreased. Finally, numerical examples illustrated to justify the proposed model.

Project description:BackgroundDifferential abundance analysis (DAA) is one central statistical task in microbiome data analysis. A robust and powerful DAA tool can help identify highly confident microbial candidates for further biological validation. Numerous DAA tools have been proposed in the past decade addressing the special characteristics of microbiome data such as zero inflation and compositional effects. Disturbingly, different DAA tools could sometimes produce quite discordant results, opening to the possibility of cherry-picking the tool in favor of one's own hypothesis. To recommend the best DAA tool or practice to the field, a comprehensive evaluation, which covers as many biologically relevant scenarios as possible, is critically needed.ResultsWe performed by far the most comprehensive evaluation of existing DAA tools using real data-based simulations. We found that DAA methods explicitly addressing compositional effects such as ANCOM-BC, Aldex2, metagenomeSeq (fitFeatureModel), and DACOMP did have improved performance in false-positive control. But they are still not optimal: type 1 error inflation or low statistical power has been observed in many settings. The recent LDM method generally had the best power, but its false-positive control in the presence of strong compositional effects was not satisfactory. Overall, none of the evaluated methods is simultaneously robust, powerful, and flexible, which makes the selection of the best DAA tool difficult. To meet the analysis needs, we designed an optimized procedure, ZicoSeq, drawing on the strength of the existing DAA methods. We show that ZicoSeq generally controlled for false positives across settings, and the power was among the highest. Application of DAA methods to a large collection of real datasets revealed a similar pattern observed in simulation studies.ConclusionsBased on the benchmarking study, we conclude that none of the existing DAA methods evaluated can be applied blindly to any real microbiome dataset. The applicability of an existing DAA method depends on specific settings, which are usually unknown a priori. To circumvent the difficulty of selecting the best DAA tool in practice, we design ZicoSeq, which addresses the major challenges in DAA and remedies the drawbacks of existing DAA methods. ZicoSeq can be applied to microbiome datasets from diverse settings and is a useful DAA tool for robust microbiome biomarker discovery. Video Abstract.

Dataset Information

A Comprehensive Operation Status Evaluation Method for Mining XLPE Cables.

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A Comprehensive Operation Status Evaluation Method for Mining XLPE Cables.

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