领导者何以自我牺牲:基于道德信念视角的理论框架

以往关于领导者自我牺牲行为的研究主要关注了该领导行为的后果,而对该领导行为的形成机制缺乏系统探讨。鉴于此,本研究引入道德信念视角建构了一个领导者自我牺牲行为形成机制的理论框架,剖析了两种领导者自我牺牲动机,探讨了基于个体因素(人格特质、自我建构)和环境因素(组织文化)的领导者自我牺牲行为形成机制。分析表明:义务和权利动机均会驱动领导者自我牺牲行为;共生和互依自我建构领导者经由义务道德信念和义务动机展现自我牺牲行为,而能动和独立自我建构领导者经由权利道德信念和权利动机展现自我牺牲行为;嵌入性和等级结构组织文化经由义务道德信念和义务动机促使领导者展现自我牺牲行为,而自主性和平等主义组织文化经由权利道德信念和权利动机促使领导者展现自我牺牲行为。本研究有助于推动领导者自我牺牲行为形成机制的研究,为组织促进领导者展现自我牺牲行为提供理论指导。

EMCO-based Optimal Layout Design of Hybrid Wind-wave Energy Converters Array

Marine renewable energy,combining wave energy converters(WECs)and floating wind turbines(FWTs)into hybrid wave-wind energy converters(HWWECs),garners significant global interest.HWWECs offer potential cost reductions,increased power generation,and enhanced system stability.The absorption power of high wind energy sites is primarily influenced by the complex hydrodynamic interactions among floating bodies,which are closely related to the location and wind-wave environment of high wind energy sites.To delve into the positive interactions among HWWECs,this paper proposes a HWWEC array optimization strategy based on the artificial ecosystem opti-mization-manta ray foraging optimization coordinated op-timizer(EMCO).In EMCO,the decomposition operator of artificial ecosystem optimization(AEO)and the flip-ping-dipper foraging operator of manta ray foraging opti-mization coordinated(MRFO)cooperate dynamically to effectively balance local exploitation and global exploration.To validate the effectiveness of EMCO,experiments were conducted in scenarios with 3,5,8,and 20 HWWECs,and compared with five typical algorithms.Experimental results demonstrate the existence of multiple optimal solutions for HWWEC arrays.EMCO achieves maximum total absorp-tion power and exhibits good stability.Notably,EMCO en-hances the q-factor values of HWWECs across four scales:1.0478,1.0586,1.0612,and 0.9965,respectively.Index Terms—Marine renewable energy,hybrid wind-wave energy converter,layout optimization,coordinating optimizer.

A critical survey of technologies of large offshore wind farm integration: summary, advances, and perspectives

Offshore wind farms(OWFs)have received widespread attention for their abundant unexploited wind energy poten-tial and convenient locations conditions.They are rapidly developing towards having large capacity and being located further away from shore.It is thus necessary to explore effective power transmission technologies to connect large OWFs to onshore grids.At present,three types of power transmission technologies have been proposed for large OWF integration.They are:high voltage alternating current(HVAC)transmission,high voltage direct current(HVDC)transmission,and low-frequency alternating current(LFAC)or fractional frequency alternating current transmission.This work undertakes a comprehensive review of grid connection technologies for large OWF integration.Compared with previous reviews,a more exhaustive summary is provided to elaborate HVAC,LFAC,and five HVDC topologies,consisting of line-commutated converter HVDC,voltage source converter HVDC,hybrid-HVDC,diode rectifier-based HVDC,and all DC transmission systems.The fault ride-through technologies of the grid connection schemes are also presented in detail to provide research references and guidelines for researchers.In addition,a comprehensive evalu-ation of the seven grid connection technologies for large OWFs is proposed based on eight specific indicators.Finally,eight conclusions and six perspectives are outlined for future research in integrating large OWFs.

GTB-PPI:Predict Protein-protein Interactions Based on L1-regularized Logistic Regression and Gradient Tree Boosting

Protein-protein interactions(PPIs)are of great importance to understand genetic mechanisms,delineate disease pathogenesis,and guide drug design.With the increase of PPI data and development of machine learning technologies,prediction and identification of PPIs have become a research hotspot in proteomics.In this study,we propose a new prediction pipeline for PPIs based on gradient tree boosting(GTB).First,the initial feature vector is extracted by fusing pseudo amino acid composition(Pse AAC),pseudo position-specific scoring matrix(Pse PSSM),reduced sequence and index-vectors(RSIV),and autocorrelation descriptor(AD).Second,to remove redundancy and noise,we employ L1-regularized logistic regression(L1-RLR)to select an optimal feature subset.Finally,GTB-PPI model is constructed.Five-fold cross-validation showed that GTB-PPI achieved the accuracies of 95.15% and 90.47% on Saccharomyces cerevisiae and Helicobacter pylori datasets,respectively.In addition,GTB-PPI could be applied to predict the independent test datasets for Caenorhabditis elegans,Escherichia coli,Homo sapiens,and Mus musculus,the one-core PPI network for CD9,and the crossover PPI network for the Wnt-related signaling pathways.The results show that GTB-PPI can significantly improve accuracy of PPI prediction.The code and datasets of GTB-PPI can be downloaded from https://github.com/QUST-AIBBDRC/GTB-PPI/.

Intelligent reasoning and management decision making with grey rough influence diagrams

Purpose-Influence diagrams(IDs)have been widely applied as a form of knowledge expression and a decision analysis tool in the management and engineering fields.Relationship measurements and expectation values are computed depending on probability distributions in traditional IDs,however,most information systems in the real world are nondeterministic,and data in information tables can be interval valued,multiple valued and even incomplete.Consequently,conventional numeric models of IDs are not suitable for information processing with respect to imprecise data whose boundaries are uncertain.The paper aims to discuss these issues.Design/methodology/approach-The grey system theory and rough sets have proved to be effective tools in the data processing of uncertain information systems,approximate knowledge acquisition and representation are also the objectives in intelligent reasoning and decision analysis.Hence,this study proposes a new mathematical model by combining grey rough sets with IDs,and approximate measurements are used instead of probability distribution,an implicational relationship is utilized instead of an indiscernible relationship,and all of the features of the proposed approach contribute to deal with uncertain problems.Findings-The focus of this paper is to provide a more comprehensive framework for approximate knowledge representation and intelligent decision analysis in uncertain information systems and an example of decision support in product management systems with the new approach is illustrated.Originality/value-Collaboration of IDs and grey rough sets is first proposed,which provides a new mathematical and graphical tool for approximate reasoning and intelligent decision analysis within interval-valued information systems.