Vulnerability of multinational corporation knowledge network facing resource loss

PurposeThe vulnerability of multinational corporation (MNC) knowledge network is one of the major causes for the failure and even the death of MNCs in the fierce global market competition. Employee turnover and knowledge loss are the triggers for the MNC knowledge network vulnerability and a matter of serious concern in the evolution and development of MNC knowledge network. The purpose of this work is to propose a valid and quantitative measurement method to investigate the influence of employee loss and knowledge loss on the vulnerability of MNC knowledge network.Design/methodology/approachMNC knowledge network is inherently a heterogeneous network where there are mainly two types of units: employees and their knowledge. Therefore, this paper establishes a weighted super-network model for MNC knowledge network to depict its heterogeneous composition. On the basis of the weighted MNC knowledge super-network, the static and dynamic vulnerability measurement methods are further proposed to investigate and evaluate MNC knowledge network vulnerability.FindingsA real case is given to illustrate the applicability of the proposed weighted MNC knowledge super-network model and the network vulnerability measurement methods. The results show the super-network model proposed in this paper can effectively embody the complex features of MNC knowledge network, and the vulnerability measurement methods can effectively investigate the influence of employee loss and knowledge loss on network vulnerability.Originality/valueFrom the perspective of super-network, researchers and practitioners can get a more systematic and deeper understanding of the MNC knowledge network and its human and knowledge resource constitute which are vital for the evolution and development of MNC. Moreover, the MNC knowledge network vulnerability measurement methods can effectively measure and analyze the influence of resource loss on network vulnerability, which can provide a helpful decision support for monitoring and managing of MNC knowledge network vulnerability to reduce its adverse effects.

Rapid Disaster Data Dissemination and Vulnerability Assessment through Synthesis of a Web-Based Extreme Event Viewer and Deep Learning

Infrastructure vulnerability has drawn significant attention in recent years, partly because of the occurrence of low-probability and high-consequence disruptive events such as 2017 hurricanes Harvey, Irma, and Maria, 2011 Tuscaloosa and Joplin tornadoes, and 2015 Gorkha, Nepal, and 2017 Central Mexico earthquakes. Civil infrastructure systems support social welfare, thus viability and sustained operation is critical. A variety of frameworks, models, and tools exist for advancing infrastructure vulnerability research. Nevertheless, providing accurate vulnerability measurement remains challenging. This paper presents a state-of-the-art data collection and information extraction methodology to document infrastructure at high granularity to assess preevent vulnerability and postevent damage in the face of disasters. The methods establish a baseline of preevent infrastructure functionality that can be used to measure impacts and temporal recovery following a disaster. The Extreme Events Web Viewer (EEWV) presented as part of the methodology is a GIS-based web repository storing spatial and temporal data describing communities before and after disasters and facilitating data analysis techniques. This web platform can store multiple geolocated data formats including photographs and 360° videos. A tool for automated extraction of photography from 360° video data at locations of interest specified in the EEWV was created to streamline data utility. The extracted imagery provides a manageable data set to efficiently document characteristics of the built and natural environment. The methodology was tested to locate buildings vulnerable to flood and storm surge on Dauphin Island, Alabama. Approximately 1,950 buildings were passively documented with vehicle-mounted 360° video. Extracted building images were used to train a deep learning neural network to predict whether a building was elevated or nonelevated. The model was validated, and methods for iterative neural network training are described. The methodology, from rapidly collecting large passive datasets, storing the data in an open repository, extracting manageable datasets, and obtaining information from data through deep learning, will facilitate vulnerability and postdisaster analyses as well as longitudinal recovery measurement.

Penilaian Kerentanan Air Permukaan terhadap Pencemaran di Sub DAS Garang Hilir Berbasis Multi-Indeks

Assessing the surface water vulnerability to pollution in the Garang Downstream Watershed Semarang requires a study concerned with some environmental components/indicators. Vulnerability measurement through surface water susceptibility index formulation on pollution is important considering the absence of surface water pollution effect indicators in an efficient assessment system. Therefore, a multi-indicator vulnerability assessment on surface water pollution is necessary. The Surface Water Vulnerability Index to Pollution (SWVIP) is composed of five components, namely water quality (WQ), rainfall (R), land use and vegetation cover (LVC), river hydrogeometric (RH) and population (P). Regarding index development, the subindex graphs and the weighting of each component are created. The application of composite index measurement yields an equation of SWVIP = 0.29.WQI + 0.23PI + 0.14RI + 0.20.LVCI + 0.14.RHI and an index value of 73.87 including the "rather high" category that represents the "vulnerable"condition in the Garang Downstream Watershed Semarang. This suggests that the five selected components used in the index creation can provide useful information to decision making in the surface water pollution control.

Penilaian Kerentanan Air Permukaan terhadap Pencemaran di Sub DAS Garang Hilir Berbasis Multi-Indeks

Assessing the surface water vulnerability to pollution in the Garang Downstream Watershed Semarang requires a study concerned with some environmental components/indicators. Vulnerability measurement through surface water susceptibility index formulation on pollution is important considering the absence of surface water pollution effect indicators in an efficient assessment system. Therefore, a multi-indicator vulnerability assessment on surface water pollution is necessary. The Surface Water Vulnerability Index to Pollution (SWVIP) is composed of five components, namely water quality (WQ), rainfall (R), land use and vegetation cover (LVC), river hydrogeometric (RH) and population (P). Regarding index development, the subindex graphs and the weighting of each component are created. The application of composite index measurement yields an equation of SWVIP = 0.29.WQI + 0.23PI + 0.14RI + 0.20.LVCI + 0.14.RHI and an index value of 73.87 including the "rather high" category that represents the "vulnerable"condition in the Garang Downstream Watershed Semarang. This suggests that the five selected components used in the index creation can provide useful information to decision making in the surface water pollution control.