Journal articles: 'Critical network'

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&NA;. "Critical Care NETWORK." Dimensions of Critical Care Nursing 4, no. 1 (January 1985): 7–9. http://dx.doi.org/10.1097/00003465-198501000-00002.

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&NA;. "Critical Care Network." Dimensions of Critical Care Nursing 4, no. 2 (March 1985): 69. http://dx.doi.org/10.1097/00003465-198503000-00002.

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&NA;. "Critical Care NETWORK." Dimensions of Critical Care Nursing 4, no. 3 (May 1985): 135–37. http://dx.doi.org/10.1097/00003465-198505000-00002.

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&NA;. "Critical Care NETWORK." Dimensions of Critical Care Nursing 4, no. 5 (September 1985): 261–63. http://dx.doi.org/10.1097/00003465-198509000-00002.

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&NA;. "Critical Care NETWORK." Dimensions of Critical Care Nursing 5, no. 1 (January 1986): 5–7. http://dx.doi.org/10.1097/00003465-198601000-00002.

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&NA;. "Critical Care NETWORK." Dimensions of Critical Care Nursing 5, no. 2 (March 1986): 70. http://dx.doi.org/10.1097/00003465-198603000-00002.

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&NA;. "Critical Care NETWORK." Dimensions of Critical Care Nursing 5, no. 3 (May 1986): 134–35. http://dx.doi.org/10.1097/00003465-198605000-00002.

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&NA;. "Critical Care NETWORK." Dimensions of Critical Care Nursing 5, no. 4 (July 1986): 194. http://dx.doi.org/10.1097/00003465-198607000-00001.

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&NA;. "Critical Care NETWORK." Dimensions of Critical Care Nursing 5, no. 5 (September 1986): 260–62. http://dx.doi.org/10.1097/00003465-198609000-00002.

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&NA;. "Critical Care NETWORK." Dimensions of Critical Care Nursing 5, no. 6 (November 1986): 327. http://dx.doi.org/10.1097/00003465-198611000-00002.

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&NA;. "Critical Care NETWORK." Dimensions of Critical Care Nursing 6, no. 2 (March 1987): 69. http://dx.doi.org/10.1097/00003465-198703000-00002.

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&NA;. "Critical Care NETWORK." Dimensions of Critical Care Nursing 6, no. 3 (May 1987): 134–35. http://dx.doi.org/10.1097/00003465-198705000-00002.

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&NA;. "Critical Care NETWORK." Dimensions of Critical Care Nursing 6, no. 4 (July 1987): 198–201. http://dx.doi.org/10.1097/00003465-198707000-00002.

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&NA;. "Critical Care NETWORK." Dimensions of Critical Care Nursing 6, no. 5 (September 1987): 261–63. http://dx.doi.org/10.1097/00003465-198709000-00002.

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&NA;. "Critical Care NETWORK." Dimensions of Critical Care Nursing 6, no. 6 (November 1987): 325–26. http://dx.doi.org/10.1097/00003465-198711000-00002.

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&NA;. "Critical Care NETWORK." Dimensions of Critical Care Nursing 7, no. 1 (January 1988): 5–7. http://dx.doi.org/10.1097/00003465-198801000-00002.

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&NA;. "Critical Care NETWORK." Dimensions Of Critical Care Nursing 7, no. 2 (March 1988): 67–69. http://dx.doi.org/10.1097/00003465-198803000-00001.

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&NA;. "Critical Care NETWORK." Dimensions of Critical Care Nursing 7, no. 4 (July 1988): 195. http://dx.doi.org/10.1097/00003465-198807000-00001.

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&NA;. "Critical Care NETWORK." Dimensions of Critical Care Nursing 7, no. 5 (September 1988): 261. http://dx.doi.org/10.1097/00003465-198809000-00002.

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&NA;. "Critical Care NETWORK." Dimensions Of Critical Care Nursing 7, no. 6 (November 1988): 323. http://dx.doi.org/10.1097/00003465-198811000-00001.

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&NA;. "Critical Care NETWORK." Dimensions Of Critical Care Nursing 8, no. 2 (March 1989): 69–71. http://dx.doi.org/10.1097/00003465-198903000-00002.

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&NA;. "Critical Care NETWORK." Dimensions Of Critical Care Nursing 8, no. 3 (May 1989): 133. http://dx.doi.org/10.1097/00003465-198905000-00003.

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&NA;. "Critical Care NETWORK." Dimensions of Critical Care Nursing 8, no. 4 (July 1989): 196–99. http://dx.doi.org/10.1097/00003465-198907000-00001.

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&NA;. "Critical Care NETWORK." Dimensions of Critical Care Nursing 8, no. 5 (September 1989): 265. http://dx.doi.org/10.1097/00003465-198909000-00002.

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&NA;. "Critical Care NETWORK." Dimensions Of Critical Care Nursing 8, no. 6 (November 1989): 329. http://dx.doi.org/10.1097/00003465-198911000-00002.

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&NA;. "Critical Care NETWORK." Dimensions Of Critical Care Nursing 9, no. 1 (January 1990): 5. http://dx.doi.org/10.1097/00003465-199001000-00002.

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&NA;. "Critical Care NETWORK." Dimensions of Critical Care Nursing 9, no. 2 (March 1990): 65–67. http://dx.doi.org/10.1097/00003465-199003000-00001.

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&NA;. "Critical Care NETWORK." Dimensions of Critical Care Nursing 9, no. 3 (May 1990): 128–29. http://dx.doi.org/10.1097/00003465-199005000-00002.

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&NA;. "Critical Care NETWORK." Dimensions of Critical Care Nursing 9, no. 4 (July 1990): 189. http://dx.doi.org/10.1097/00003465-199007000-00001.

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&NA;. "Critical Care NETWORK." Dimensions of Critical Care Nursing 10, no. 1 (January 1991): 41. http://dx.doi.org/10.1097/00003465-199101000-00009.

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&NA;. "Critical Care NETWORK." Dimensions of Critical Care Nursing 10, no. 3 (May 1991): 185. http://dx.doi.org/10.1097/00003465-199105000-00013.

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&NA;. "Critical Care NETWORK." Dimensions Of Critical Care Nursing 10, no. 4 (July 1991): 187. http://dx.doi.org/10.1097/00003465-199107000-00001.

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&NA;. "Critical Care Network." Dimensions of Critical Care Nursing 10, no. 5 (September 1991): 307–8. http://dx.doi.org/10.1097/00003465-199109000-00025.

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&NA;. "Critical Care NETWORK." Dimensions of Critical Care Nursing 10, no. 6 (November 1991): 311. http://dx.doi.org/10.1097/00003465-199111000-00001.

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Larequi, Yves. "CPN Critical Physiotherapy Network." Mains Libres, no. 3 (2024): 215. http://dx.doi.org/10.55498/mainslibres.2024.12.3.215.

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Abinaya, B., and E. C. Henry Amirtharaj. "An Alternative Method for Finding the Critical Path of the Network in Fuzzy Time Cost Trade off Problem." Indian Journal Of Science And Technology 17, no. 10 (March 1, 2024): 949–54. http://dx.doi.org/10.17485/ijst/v17i10.3143.

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Background : The critical path approach is used to determine the network's longest path, according to historical records. This study examines a different approach to determining the construction network's longest path. Method: Here, the network is viewed as a directed acyclic graph, and the critical path of the network is found using the longest path algorithm of the network. To find the best answer for a building project, the longest path that was found was integrated into a linear programming issue. The triangle fuzzy variable defines all of the project's inputs. The 992 square foot building area is incorporated, and three project manager’s quotes are used as a triangular fuzzy variable. Findings : This work has the options of getting quotation from the project managers, convert them as the fuzzy variables such as triangular fuzzy variable, Trapezoidal fuzzy variable and pentagonal fuzzy variable. After the network is converted into a linear programming problem using the fully fuzzy mathematical model, the best possible solution is found. Novelty and applications : Alternative method for critical path of the network has been incorporated. It has been found that the proposed method reduces the time to find the critical path of the larger networks. Keywords: Directed Acyclic Graph, Longest Path Algorithm, Triangular Fuzzy Variable, Fuzzy Linear Programming Problem, Fully Fuzzy Mathematical Model

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Dunn, Sarah, and Sean M. Wilkinson. "Identifying Critical Components in Infrastructure Networks Using Network Topology." Journal of Infrastructure Systems 19, no. 2 (June 2013): 157–65. http://dx.doi.org/10.1061/(asce)is.1943-555x.0000120.

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Yang, Haihua, and Shi An. "Critical Nodes Identification in Complex Networks." Symmetry 12, no. 1 (January 8, 2020): 123. http://dx.doi.org/10.3390/sym12010123.

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Critical nodes identification in complex networks is significance for studying the survivability and robustness of networks. The previous studies on structural hole theory uncovered that structural holes are gaps between a group of indirectly connected nodes and intermediaries that fill the holes and serve as brokers for information exchange. In this paper, we leverage the property of structural hole to design a heuristic algorithm based on local information of the network topology to identify node importance in undirected and unweighted network, whose adjacency matrix is symmetric. In the algorithm, a node with a larger degree and greater number of structural holes associated with it, achieves a higher importance ranking. Six real networks are used as test data. The experimental results show that the proposed method not only has low computational complexity, but also outperforms degree centrality, k-shell method, mapping entropy centrality, the collective influence algorithm, DDN algorithm that based on node degree and their neighbors, and random ranking method in identifying node importance for network connectivity in complex networks.

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Agostino, Deborah, Michela Arnaboldi, and Martina Dal Molin. "Critical crossroads to explain network change: evidence from a goal-directed network." International Journal of Public Sector Management 30, no. 3 (April 10, 2017): 255–69. http://dx.doi.org/10.1108/ijpsm-04-2016-0078.

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Purpose Public networks studies have widely diffused in recent years, but scant attention has been devoted to network change. By endorsing the notion of critical crossroads to describe a crucial turning point for the network survival, the purpose of this paper is to investigate how and why a goal-directed network changes, considering both the benefits and the constraints of the change. Design/methodology/approach This study adopts a longitudinal case study based upon an interventionist research approach (Jönsson and Lukka, 2006), with the researchers being immersed in the network life of a group of Italian public universities over a period of 17 years. Findings This paper proposes an empirical derived framework about network evolution that identifies two different types of crossroads (i.e. resource-driven crossroads and management driven) as drivers for network evolution. The main determinant behind these crisis situation were found in the heterogeneity of the network actors and, while overcoming the crossroads, informal sub-networks were found emerging. Originality/value This study enlarges current public network literature by focusing specifically on how and why networks change, an aspect underinvestigated by current literature.

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Liu, Xiangrong, Zengyan Hong, Juan Liu, Yuan Lin, Alfonso Rodríguez-Patón, Quan Zou, and Xiangxiang Zeng. "Computational methods for identifying the critical nodes in biological networks." Briefings in Bioinformatics 21, no. 2 (February 12, 2019): 486–97. http://dx.doi.org/10.1093/bib/bbz011.

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Abstract A biological network is complex. A group of critical nodes determines the quality and state of such a network. Increasing studies have shown that diseases and biological networks are closely and mutually related and that certain diseases are often caused by errors occurring in certain nodes in biological networks. Thus, studying biological networks and identifying critical nodes can help determine the key targets in treating diseases. The problem is how to find the critical nodes in a network efficiently and with low cost. Existing experimental methods in identifying critical nodes generally require much time, manpower and money. Accordingly, many scientists are attempting to solve this problem by researching efficient and low-cost computing methods. To facilitate calculations, biological networks are often modeled as several common networks. In this review, we classify biological networks according to the network types used by several kinds of common computational methods and introduce the computational methods used by each type of network.

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Zhang, Tao, Xiangyun Tang, Jiacheng Wang, and Jiqiang Liu. "Network Security Management in Heterogeneous Networks." Electronics 14, no. 3 (January 31, 2025): 568. https://doi.org/10.3390/electronics14030568.

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Dasari, Venkat R., Brian Jalaian, and Saleil Bhat. "Programmable control plane for mission critical wireless networks." Journal of Defense Modeling and Simulation: Applications, Methodology, Technology 15, no. 2 (August 24, 2017): 245–54. http://dx.doi.org/10.1177/1548512917715808.

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The control plane is an essential element to manage the data plane communications in wired and wireless networks. In a traditional network architecture, the control plane is embedded in the hardware and it lacks programmability. Military wireless networks are historically heterogeneous in nature and require complicated manual setups to create interoperability because of a discontinuous control plane. In the present work, we describe a simple programmable control plane model along with associated network abstractions to create a unified control plane interface that can communicate across heterogeneous wireless networks. We chose an ns-3 based network simulation engine to create, test, and validate the functional fidelity of our models. In addition to the network objects and interfaces available in ns-3, we modified ns-3 codes to capture the characteristics of our proposed model. Furthermore, we proposed a tractable mathematical framework to optimize the performance of the proposed control plane model.

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Huang, Wenli, Liang Chen, and Junli Li. "A Critical Candidate Node-Based Attack Model of Network Controllability." Entropy 26, no. 7 (July 8, 2024): 580. http://dx.doi.org/10.3390/e26070580.

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The controllability of complex networks is a core issue in network research. Assessing the controllability robustness of networks under destructive attacks holds significant practical importance. This paper studies the controllability of networks from the perspective of malicious attacks. A novel attack model is proposed to evaluate and challenge network controllability. This method disrupts network controllability with high precision by identifying and targeting critical candidate nodes. The model is compared with traditional attack methods, including degree-based, betweenness-based, closeness-based, pagerank-based, and hierarchical attacks. Results show that the model outperforms these methods in both disruption effectiveness and computational efficiency. Extensive experiments on both synthetic and real-world networks validate the superior performance of this approach. This study provides valuable insights for identifying key nodes crucial for maintaining network controllability. It also offers a solid framework for enhancing network resilience against malicious attacks.

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BAYRAK, TUNCAY, and MARTHA R. GRABOWSKI. "SAFETY-CRITICAL WIDE AREA NETWORK PERFORMANCE EVALUATION." International Journal of Information Technology & Decision Making 02, no. 04 (December 2003): 651–67. http://dx.doi.org/10.1142/s0219622003000823.

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There has been a considerable amount of research in the area of network performance evaluation. However, little of the research is focused on the evaluation of real-time safety-critical WANs, a need that motivated this research. Over the years, networks have been evaluated by different disciplines from different perspectives. Many of these evaluations focus on network technical performance, or an organization's performance when using a network, or individual users' performance when using a network. In this study, network performance was measured using empirical data from an operational WAN and by utilizing well-defined and well-known network performance metrics such as reliability, availability, and response time. In general, increased use of a real-time WAN in this study was associated with negative impacts on WAN performance and increased redundancy was generally associated with positive impacts, allowing greater system usage and higher network workload, as intended. The impacts of increasing redundancy on MTBF were mixed, as were the MTTR impacts; availability values varied considerably by port. The network performance data thus shows mixed empirical results from increases in network usage and redundancy, which highlights the importance of managing and measuring network performance at both the system and the local level.

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Liu, Chih-Hsing (Sam), Bernard Gan, and Yucheng Eason Zhang. "Why “they” occupies the critical network positions?" Management Decision 53, no. 1 (February 9, 2015): 100–123. http://dx.doi.org/10.1108/md-04-2014-0186.

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Purpose – The purpose of this paper is to draw on social network theory to develop a new theoretical model to explain how experience and leadership influence critical network position. Broad analyses of the mediating role of leadership between experience and critical network position calls attention to the need to investigate the direct relationship between leadership and critical network position. Empirical examinations of the roles of leadership and experience within the social network context are lacking. The authors seeks to fill this gap by constructing a new theoretical model and testing it in the knowledge-intensive sector. Design/methodology/approach – The authors made 3,356 observations involving 427 faculty members in business and management departments in Taiwanese universities. To test the model, the authors performed two different regression models using the Baron and Kenny (1986) procedure and the Sobel test. Findings – The results that the authors obtained lead to three conclusions. First, scholars’ experience positively relates to leadership in grouping the followers toward the common goal and to their publications. Second, scholars’ leadership predicts occupying the critical network position, which, in turn, facilitates acquiring more information and resources. Third, experience relates to critical network position through influence on personal leadership. Research limitations/implications – Although the insights gained from the study are important in theoretical and empirical implications, this study has its limitations. The research examined the professional interaction networks of business management scholars of Taiwanese universities. Although the authors believe that the findings are generalizable to other fields of similar phenomena settings, there are also settings where the generalizability of the study is probably quite limited. Future research could study samples in other fields, such as biomedical research and chemical research, and samples in other knowledge-intensive industries. Practical implications – The results imply that managers can reap the benefits of their leadership either informal or informal organizations by occupying a critical position to control the flow of resources and information. In other words, the results indicate that leadership can be developed through experience. As well, leadership plays an important mediating role between experience and critical network positions. This may seem to contradict the evidence from research on network concepts and resources control, which primarily draws attention to leadership being a helpful quality for those in critical network positions. Originality/value – This research extends beyond previous studies that focussed on the value of critical network positions. Furthermore, the paper also examines how the relationship between prior relevant experience and leadership plays a role in academic network settings. To the authors’ knowledge, no studies explore this perspective. Finally, studies that examine the relationship between business management academic networks and different methodology used to measures the network position are few in number, and those that use such longitudinal empirical work are particularly lacking. This study addresses these issues.

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BAYRAK, TUNCAY, and MARTHA R. GRABOWSKI. "NETWORK PERFORMANCE IMPACTS ON OPERATORS IN SAFETY-CRITICAL SYSTEMS." International Journal of Information Technology & Decision Making 05, no. 01 (March 2006): 173–94. http://dx.doi.org/10.1142/s0219622006001836.

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Safety-critical wide area networks are comprised of human and technical elements cooperatively performing tasks in a safety-critical setting. In such settings, both human and technical dimensions are critical in performance evaluation. It is the relationships between changes in network performance and their impacts on human operator performance with safety-critical wide area networks that we investigate in this research. The paper begins by examining the theoretical background for this research, and then describes the research model. It was found that decreases in network reliability were associated with expected degradations in operator satisfaction, operator confidence, and increases in operator workload. Likewise, it was found that an increased number of network tasks processed was associated with a decrease in operator accuracy and decreased operator communication. The results of this study suggest that understanding the impact of the network performance on human performance is important in safety-critical settings employing wide-area networks.

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Jana, Debasish, Sven Malama, Sriram Narasimhan, and Ertugrul Taciroglu. "Edge-based graph neural network for ranking critical road segments in a network." PLOS ONE 18, no. 12 (December 21, 2023): e0296045. http://dx.doi.org/10.1371/journal.pone.0296045.

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Transportation networks play a crucial role in society by enabling the smooth movement of people and goods during regular times and acting as arteries for evacuations during catastrophes and natural disasters. Identifying the critical road segments in a large and complex network is essential for planners and emergency managers to enhance the network’s efficiency, robustness, and resilience to such stressors. We propose a novel approach to rapidly identify critical and vital network components (road segments in a transportation network) for resilience improvement or post-disaster recovery. We pose the transportation network as a graph with roads as edges and intersections as nodes and deploy a Graph Neural Network (GNN) trained on a broad range of network parameter changes and disruption events to rank the importance of road segments. The trained GNN model can rapidly estimate the criticality rank of individual road segments in the modified network resulting from an interruption. We address two main limitations in the existing literature that can arise in capital planning or during emergencies: ranking a complete network after changes to components and addressing situations in post-disaster recovery sequencing where some critical segments cannot be recovered. Importantly, our approach overcomes the computational overhead associated with the repeated calculation of network performance metrics, which can limit its use in large networks. To highlight scenarios where our method can prove beneficial, we present examples of synthetic graphs and two real-world transportation networks. Through these examples, we show how our method can support planners and emergency managers in undertaking rapid decisions for planning infrastructure hardening measures in large networks or during emergencies, which otherwise would require repeated ranking calculations for the entire network.

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Raganowicz , Andrzej. "Critical Condition of Sewage Network." Geomatics and Environmental Engineering 12, no. 1 (2018): 91. http://dx.doi.org/10.7494/geom.2018.12.1.91.

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Bowker, Samantha L., Henry T. Stelfox, and Sean M. Bagshaw. "Critical Care Strategic Clinical Network." Canadian Medical Association Journal 191, Suppl (December 4, 2019): S22—S23. http://dx.doi.org/10.1503/cmaj.190578.

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Kren, Kari J., and Joan Schwaba. "P76– The Critical Illness Network." Otolaryngology–Head and Neck Surgery 143, no. 1_suppl (July 2010): 117. http://dx.doi.org/10.1016/j.otohns.2010.04.100.

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Journal articles on the topic 'Critical network'

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