The narcissism spectrum model synthesizes extensive personality, social–psychological, and clinical evidence, building on existing knowledge about narcissistic grandiosity and vulnerability to reveal ...a view of narcissism that respects its clinical origins, embraces the diversity and complexity of its expression, and reflects extensive scientific evidence about the continuity between normal and abnormal personality expression. Critically, the proposed model addresses three key, inter-related problems that have plagued narcissism scholarship for more than a century. These problems can be summarized as follows: (a) What are the key features of narcissism? (b) How are they organized and related to each other? and (c) Why are they organized that way, that is, what accounts for their relationships? By conceptualizing narcissistic traits as manifested in transactional processes between individuals and their social environments, the model enables integration of existing theories of narcissism and thus provides a compelling perspective for future examination of narcissism and its developmental pathways.
Importance–performance analysis (IPA) is extensively used in hospitality and tourism research because of its simplicity. However, due to the lack of critical statistical analysis, the traditional IPA ...framework is compromised by serious reliability and validity issues. Although many researchers have tried to address these problems, a comprehensive framework to guide researchers through the various stages of IPA is still needed. This study offers a research framework and a straightforward guide for the use of IPA. Experimental surveys are conducted to validate the proposed research framework.
•Offer a research framework and a straightforward guide for the use of IPA.•Provide solutions for some critical issues in conducting IPA studies.•Show how to perform IPA incorporating reliability and validity measures.•Conduct experimental surveys to validate the research framework.
•The MTS network model based on the main ports and routes is established.•State of the post-disaster MTS is analyzed and the residual resilience is proposed.•Some residual resilience importance ...methods for the post-disaster MTS are given.•23 cities’ sea routes are used to demonstrate the applicability of the proposed method.
In maritime transportation system (MTS), ports and ocean routes are essential for establishing and maintaining effective international trade routes. However, the ability of the ports to send and receive goods can be easily destroyed by political and natural interferences. This will cause a significant negative socio-economic impact such as port operation suspension and route disruption. Effectively implementing resilience management in MTS can therefore improve its ability to handle interruptions and minimizing losses. Based on the post-disaster analysis, this paper proposes a new method to optimize the residual resilience management of ports and routes in MTS and proposes an optimal resilience model. The residual resilience is then applied to some importance measures. The Copeland method is used to comprehensively rank the importance of ports and routes. The restoration priority of interrupted ports and routes of different importance measures for the purpose of minimizing residual resilience is also studied. Sea routes consisting of 23 cities are used to demonstrate the applicability of the proposed method.
Component importance measures are widely used in engineering and reliability analysis in testing the safety of running systems. There are several component importance measures to realize which ...components in a coherent system play a more important role than the others. The most common ones belong to Birnbaum and Barlow & Proschan. In this paper a new method, based on the number of path sets with exactly i working components such that the component at sth place functions, has been proposed to evaluate these two measures for all types of systems appearing in the literature.
Corporate financial distress prediction research has been ongoing for more than half a century, during which many models have emerged, among which ensemble learning algorithms are the most accurate. ...Most of the state-of-the-art methods of recent years are based on gradient boosted decision trees. However, most of them do not consider using feature importance for feature selection, and a few of them use the feature importance method with bias, which may not reflect the true importance of features. To solve this problem, a heuristic algorithm based on permutation importance (PIMP) is proposed to modify the biased feature importance measure in this paper. This method ranks and filters the features used by machine learning models, which not only improves accuracy but also makes the results more interpretable. Based on financial data from 4,167 listed companies in China between 2001 and 2019, the experiment shows that compared with using the random forest (RF) wrapper method alone, the bias in feature importance is indeed corrected by combining the PIMP method. After the redundant features are removed, the performance of most machine learning models is improved. The PIMP method is a promising addition to the existing financial distress prediction methods. Moreover, compared with traditional statistical learning models and other machine learning models, the proposed PIMP-XGBoost offers higher prediction accuracy and clearer interpretation, making it suitable for commercial use.
•The model Combines a corrected feature selection measure and XGBoost.•Permutation importance can correct the bias of feature importance.•The model is validated on Chinese listed companies datasets over five metrics.•The model is proved to outperform several benchmark techniques.•The feature importance and partial dependence plot enhance model interpretation.
Currently, honey pumpkin plants are widely looked at and are in great demand for cultivation. Honey pumpkin production is in great demand with a relatively high price and can be stored for a ...relatively long time. Apomecyna saltator Fabricius (Coleoptera: Cerambycidae) as a pest of honey pumpkin in Indonesia is something that has been just known. The study about A. saltator on honey pumpkin is limited. This research was conducted to determine: the relationship between honey pumpkin plant phenology and investment of A. saltator; the effect of A. saltator infested to plant age, the length of productive period and production of honey pumpkin plants; and the importance of A. saltator on honey pumpkin plants. The study was designed with 2 treatments, namely A (honey pumpkin plants that were left exposed to A. saltator obtained by planting honey pumpkins in areas endemic to A. saltator) and B (honey pumpkin plants that were not attacked by A. saltator obtained by wrapping the honey pumpkin plant stems using plastic wrap from the base of the stem which was applied from 7 days after planting and continued every day in line with plant growth until the wrapped stems were 1.5 m long). The treatments were in five replications. Observation variables include; plant phenology, symptoms of A. saltator attack, age, length of productive period, and production of honey pumpkin plants. The research shows that A. saltator investment in honey pumpkin plants occurs from the vegetative phase when the plant is 2-3 weeks old until the final generative phase (fruit ripening). The attack of A. saltator had a significant effect on reducing age, length of productive period and production of honey pumpkin plants. Based on the pest economic meaning, A. saltator is classified as an important pest of honey pumpkin plants.
Multiple importance sampling (MIS) methods use a set of proposal distributions from which samples are drawn. Each sample is then assigned an importance weight that can be obtained according to ...different strategies. This work is motivated by the trade-off between variance reduction and computational complexity of the different approaches (classical vs. deterministic mixture) available for the weight calculation. A new method that achieves an efficient compromise between both factors is introduced in this letter. It is based on forming a partition of the set of proposal distributions and computing the weights accordingly. Computer simulations show the excellent performance of the associated partial deterministic mixture MIS estimator.
•A novel cost-constrained reliability importance is proposed.•Mechanisms of importance to guide the design of optimization rules are explored.•A solving algorithm integrating the advantages of ...importance and GA is developed.
In the field of reliability engineering, importance measures are widely used to prioritize components within a system and facilitate the improvement of system performance. However, current multi-component importance measures, such as joint reliability importances (JRIs) and their extensions, do not fully account for the potential impact of limited resource constraints, which can significantly impede efforts to improve system reliability. To address this issue, this paper proposes a novel JRI of two components for the cost-constrained reliability optimization model (ROM), which incorporates constraint factors into the JRI calculation. This new JRI can be used to evaluate the interaction effect of two components on system reliability under cost constraints. Subsequently, a cost-constrained, ROM-based, mixed reliability importance (CRMRI) is introduced by integrating the features of single-component importance measures with the newly devised JRI. Given equivalent costs for improving each component, the CRMRI approach can identify the two components whose simultaneous improvement contributes the most to enhancing system reliability. Lastly, we develop a CRMRI-based genetic algorithm (CRMGA) to solve the cost-constrained ROM. Experimental results on systems of various scales demonstrate that CRMGA can produce superior solutions with faster convergence speed, enhanced robustness, and higher efficiency compared to other optimization algorithms.
Importance–Performance Analysis (IPA) is a simple and useful technique for identifying those attributes of a product or service that are most in need of improvement or that are candidates for ...possible cost-saving conditions without significant detriment to overall quality. To this end, a two-dimensional IPA grid displayed the results of the evaluation about importance and performance of each relevant attribute. This paper shows that ordinal preferences are better than metric measures of the importance dimension and proposes a formula to transform the ordinal measure into a new metric scale adapted to the IPA grid. This formula makes allowances for the total number of features considered, the number of rankings, and the reported orders of preference.
Classifier specific (CS) and classifier agnostic (CA) feature importance methods are widely used (often interchangeably) by prior studies to derive feature importance ranks from a defect classifier. ...However, different feature importance methods are likely to compute different feature importance ranks even for the same dataset and classifier. Hence such interchangeable use of feature importance methods can lead to conclusion instabilities unless there is a strong agreement among different methods. Therefore, in this paper, we evaluate the agreement between the feature importance ranks associated with the studied classifiers through a case study of 18 software projects and six commonly used classifiers. We find that: 1) The computed feature importance ranks by CA and CS methods do not always strongly agree with each other. 2) The computed feature importance ranks by the studied CA methods exhibit a strong agreement including the features reported at top-1 and top-3 ranks for a given dataset and classifier, while even the commonly used CS methods yield vastly different feature importance ranks. Such findings raise concerns about the stability of conclusions across replicated studies. We further observe that the commonly used defect datasets are rife with feature interactions and these feature interactions impact the computed feature importance ranks of the CS methods (not the CA methods). We demonstrate that removing these feature interactions, even with simple methods like CFS improves agreement between the computed feature importance ranks of CA and CS methods. In light of our findings, we provide guidelines for stakeholders and practitioners when performing model interpretation and directions for future research, e.g., future research is needed to investigate the impact of advanced feature interaction removal methods on computed feature importance ranks of different CS methods.
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