Network Diffusion and Place Formation

Highway bridges play a vital link role in surface transportation network, and their failures not only cause disruption of service but also danger people life. Seismic base isolation is an important technique that is used for reduce the seismic vulnerability by decreasing the seismic demand instead of increasing the seismic capacity. Present work deals with study of response of bridge using seismic control systems. For present study, a two-span prestressed concrete box girder highway bridge is considered and analyzed for seismic forces using SAP2000 software. The seismic response and behavior of Box Girder Bridge is studied by linear time history analysis using Newmark’s beta method. For the analysis four different ground motion data selected and scaled for target spectra as per zone IV. The highway bridge is isolated with Lead Rubber Bearing (LRB). The response of bridge system under earthquakes has been compared with the corresponding bridge with and without the isolation system. It is observed that LRB is highly effective for controlling not only the seismic response of the bridge but also include the structural response on the cost of slight increase in the displacement of the deck.

Bridges are lifeline structures acting as an important link in surface transportation network and their collapse under seismic excitations affects social and civil functionality. Historical bridge seismic collapses under earthquake actions have proved the significant role of soil structure interaction. The paper aims at evaluating bridge protection and seismic strengthening applying isolation technique. It is based on the application of a Performance-Based Earthquake Engineering methodology, introduced by the Pacific Earthquake Engineering Research Center. The study presents a representative two—span bridge with several isolated configurations as affected by soil deformability. Isolation technique contribution is assessed in terms of costs quantities with peak ground acceleration levels. The study can be considered a first attempt to evaluate seismic effects of SSI by taking into account economic performance.

This research presents some of the key smart city strategies in Spain and the Middle East with a dual objective. Firstly, it aims to underline how the integration of information technologies in city management in the two cases altered the perception and design of the built. Secondly, it argues that they might be a useful example for the urban policy discourse in the developing countries. The two cases represent a different conception of the smart city project. The first one describes an intervention within the consolidated urban fabric of Barcelona in Spain. The second case takes place in the outskirts of Doha in Qatar. Each one was examined from the perspectives of architecture, technology, and communication and the analysis included diverse media, such as textual material, drawings, review of literature, empirical observation, and a critical elaboration of the results with the use of matrixes. Within this variety, it became easier to discuss the processes that are latent when juxtaposing studies on physical space, social practices, and real-time data with urban fabrics, culturally and historically charged. The results revealed the spatial implications that new technologies would have in diverse settings. They also hinted on the way sustainability has become part of the project for the city. Latent under this integration of space and ICT, there are user-driven actions and human interaction with artificial intelligence.

Toward the end of the 20th century, the Internet has been able to provide a large number of users with the ability to move diverse forms of information readily and thus has revolutionized business, industry, defense, science, education, research, and human interactions. In the last 10 years, sensor networking combines the technology of modern microelectronic sensors, embedded computational processing systems, and modern computer networking methodology. It is believed that sensor networking in the 21st century will be equally significant by providing measurement of the spatial-temporal physical phenomena around us, leading to a better understanding and utilization of this information in a wide range of applications. Sensor networking will be able to bring a finer-grained and fuller measurement and characterization of the world around us to be processed and communicated, so the decision makers can utilize the information to take actions in near-real-time. The potential applications enabled by SNs include security and surveillance, environmental monitoring and control, target detection and tracking, etc. Wireless sensor networks utilize the extensive networking concepts of ad hoc networks and apply them to specific sensor network scenarios. This mini-special issue comprises three papers covering three quiet diverse aspects of wireless sensor networks. The first paper, “Lifetime Maximization Based on Coverage and Connectivity in Wireless Sensor Networks,” is by T. Zhao et al. They studied the problem of network lifetime maximization for QoS specific information retrieval for the reconstruction of a spatially correlated signal field in a wireless sensor network for two wireless transmission cases. In one case, they assumed there exist single-hop transmissions between sensors and the access point, and in the other case, the measurements are sent to the access point through multi-hop transmissions. To address both of these problems, they formulated the problems using integer programming based on the theories of coverage and connectivity in sensor networks and then derived upper bounds for the network lifetime that provides benchmarks for the performance of suboptimal methods. Several lowcomplexity suboptimal algorithms for joint node scheduling and data routing were then proposed to approach the performance upper bounds. The second paper, “Using Heterogeneity to Enhance Random Walk-based Queries,” is by M. Zuniga et al. They presented a study of the impact of heterogeneous node connectivity on random walk-based queries. The main contribution of the work is showing that with a small percentage (e.g., 10%) of high-degree nodes in the network and using a simple distributed push-pull mechanism, significant cost savings can be obtained (e.g., between 30% and 70%) depending on the coverage of the high-degree nodes. Their work provides interesting theoretical results for line topologies showing that when cluster-heads have a coverage of k nodes to the right and left and are uniformly distributed, a fraction of 4/5k J Sign Process Syst (2009) 57:381–383 DOI 10.1007/s11265-009-0377-9

Robustness and Optimization of Complex Networks: Reconstructability, Algorithms and Modeling

The relation between conceptual analysis and empirical observations when ascribing or denying concepts and beliefs to non-human animals is not straightforward. In order to reflect on this relation, I focus on two theoretical proposals (Davidson’s and Allen’s) and one empirical case (vervet monkeys’ alarm calls), the three of which are permanently discussed and considered in the literature on animal cognition. First, I review briefly Davidson’s arguments for denying thought to non-linguistic animals. Second, I review Allen’s criteria for ascribing concepts to creatures capable of correcting their discriminatory powers by taking into account their previous errors. Allen affirms that this is an empirical proposal which offers good reasons, but not necessary or sufficient conditions, for concept attribution. Against Allen, I argue that his important proposal is not an empirical, but a conceptual one. Third, I resort to vervet monkeys to show that Allen’s criteria, and not Davidson’s, are very relevant for ascribing first-order and denying second-order beliefs to this species and thus make sense of the idea of animal cognition.

Gainsborough, Martin (2010). Vietnam: Rethinking the State. London, UK: Zed Books. ISBN: 978-1-84813-310-5. 224 pages.Martin Gainsborough's latest book Vietnam: Rethinking the State is a long-awaited contribution to the discussion of the role of the state in Vietnam. Gainsborough is a reader in development politics at the University of Bristol, UK, and has spent more than 20 years working and living in Vietnam as an international consultant and scholar. His analysis is consequently strongly informed by his experiences. The book itself is based on field research conducted between 1996 and 2007.As can be inferred from the title, the book aims at grasping the nature of the Vietnamese state. Yet, Gainsborough's analysis differs from those of other scholars who analyse the state by directly focusing on the "entity we call state" (p. 3) as such. Accordingly, he claims that his analytical approach differs from Weberian approaches insofar as he does not focus on the state itself, but rather applies a wider analysis of societal power relations in order to shed light on 'the state'. Gainsborough aims to advance an understanding of the state "by studying issues to do with politics and more pertinently power" (p. 2-3). He argues that focusing on the role of various societal actors and paying attention to their historical practices allows for an embedded understanding of the state. By applying his approach to the case of Vietnam, Gainsborough aims to address two key questions: "What is the nature of the state? And what is the relationship of the state to the political?" (p. 4).Gainsborough argues that most scholars who currently work on Vietnam tend to overemphasise the aspect of 'change' since the launch of doi moi (renovation) in 1986. They thereby unquestioningly accept that the changes attributed to the 'reform years', such as economic openness, private sector development, and alignment with neoliberal policies, have undermined the power of the state. As a result of his empiridoi cal analyses over the years, Gainsborough challenges such notions and makes clear that "the book is not very sympathetic towards ideas of state retreat" (p. 2). Instead, he provides a rich analysis of different empirical cases in support of his argument that the Vietnamese state has been able to maintain its central role in organising society and the economy as a whole.Rather than focusing on the overused label of 'reform' which is often understood as a move towards liberalisation, export-oriented trade, and privatisation (or 'equitisation' as called in the case of Vietnam), and thereby evoking a loss of influence of the Vietnamese state, he questions the "unmediated advance of neoliberalism" (p. 2) on a global scale. Gainsborough's analysis evolves around the empirical observation that despite formal changes, large "areas of continuity, in form of existing power structures, elite control over the economy and particular forms of rule" remain. He argues that a major reason for this is that "power continuously seeks to re-create itself" (p. 4) and in order to understand the Vietnamese state, it is crucial to detect where power structures have been re-created in order to secure existing power relations.Each chapter presents an analysis of a distinct feature of contemporary Vietnamese politics. The major issues around which the analysis evolves are the unchallenged role of the Communist Party, the phenomena of corruption and patronage, the process of privatisation, the impact of globalisation on Vietnam as well as the minor influence of neoliberal ideas on the Vietnamese state. Taken together, each chapter fulfils the role of a puzzle piece necessary to see the whole picture.Gainsborough argues that the economic and social change of the past decades did not harm the rule of the Vietnamese Communist Party because it was able to secure its central role in organising the social order. He regards this as a central reason why the Communist Party continues to rule in Vietnam today. …

We propose a mathematical framework, based on conic geometric programming, to control a susceptible-infected-susceptible viral spreading process taking place in a directed contact network with unknown contact rates. We assume that we have access to time series data describing the evolution of the spreading process observed by a collection of sensor nodes over a finite time interval. We propose a data-driven robust optimization framework to find the optimal allocation of protection resources (e.g., vaccines and/or antidotes) to eradicate the viral spread at the fastest possible rate. In contrast to current network identification heuristics, in which a single network is identified to explain the observed data, we use available data to define an uncertainty set containing all networks that are coherent with empirical observations. Through Lagrange duality and convexification of the uncertainty set, we are able to relax the robust optimization problem into a conic geometric program, recently proposed by Chandrasekaran and Shah [1] , which allows us to efficiently find the optimal allocation of resources to control the worst-case spread that can take place in the uncertainty set of networks. We illustrate our approach in a transportation network from which we collect partial data about the dynamics of a hypothetical epidemic outbreak over a finite period of time.

An approach to transportation network analysis via transferable utility games

Metro network connectivity is crucial for ensuring reliable operation of metro systems. Despite the rich literature on the connectivity analysis of transportation network, very little attention has been paid to passengers’ heterogeneous cognition toward congestion and connectivity incorporating subjective judgment. In this paper, we develop a data-driven framework to analyze metro network connectivity evolution involving individual cognition by characterizing it as a transit percolation process. The concept of individual tolerance index of congestion and a measure named network friendliness are proposed. By comparing individual tolerance index and friendliness of metro network, metro network connectivity with regard to different passengers can be depicted quantitatively. The evolution of network connectivity can be monitored both as individual tolerance changes and as time goes on. We also demonstrate how global transit breaks down when the identified bottlenecks are congested from the perspective of the passengers’ cognition. The proposed method is validated using a real-world case of the Shenzhen Metro in China. Results show that the proposed method is effective in capturing the dynamic evolution of the Shenzhen metro network connectivity and enable effective identification of transit bottlenecks. The network connectivity and friendliness are found to be significantly increased through a small improvement of the bottlenecks pinpointed.

Maps have been established as objects bestowed with knowledge, power and impact. In the hands of people, maps have been a form of political counter-power. The emergence of digital cartography, mobile media, data crowdsourcing platforms and geographic information systems strengthens the maps’ muscle and coincides with a growing interest in crisis and activist mapping, a practice that blends the capabilities of the geoweb with humanitarian assistance and campaigning. Based on empirical observation, case studies and interviews, this article analyses the emergence of digital cartography as a new paradigm in activism and humanitarianism by examining how two platforms—Ushahidi and InfoAmazonia—use maps. Ushahidi was created in 2008 in Kenya, marking the beginning of geoactivism, which employs digital cartography and often crowdsourced data to provide alternative narratives and spaces for communication and action. InfoAmazonia—dedicated to environmental issues and human rights in the Amazon region—was created in 2012. Since then, other geoactivist initiatives proliferated, presenting three different outcomes in the shape of a paradigm shift, several disruptions and criticism. This study examines these consequences, scrutinising how humanitarianism and activism—as fields of power and knowledge—are being reconfigured by new cartographic practices.

In conditions of increasing turbulence in the external environment and growing uncertainty, the issue of effective management of organizational changes in operational processes becomes especially relevant. For medium-sized enterprises, which often face limited resources, insufficient structural flexibility, and heightened levels of risk, there is a growing need for adaptive management approaches that consider both internal constraints and probabilistic external factors. In this context, a hybrid TOCDynamic model is proposed, which combines the Theory of Constraints (TOC), probability theory, and digital technologies (Big Data, IoT, AI) to create a dynamic change management system. The Theory of Constraints allows for the identification and elimination of bottlenecks that hinder the overall efficiency of the system, while the probabilistic approach supports informed decision-making under conditions of uncertainty and external environmental changes. The use of digital tools, in turn, provides the capability for real-time monitoring, forecasting, and analysis of changes. The proposed model considers the specifics of medium-sized enterprises and is based on a critical analysis of the limitations of traditional management approaches, as well as empirical observations and case studies. Its application enables increased adaptability of business processes, more efficient resource utilization, and strengthened enterprise resilience to external impacts. Moreover, it facilitates a shift from reactive to proactive change management, reducing the time lag between the emergence of changes and the response to them. The development of a culture of continuous improvement and the implementation of datadriven decisions enhances the organization's innovation potential and ensure sustainable operation in the digital age. This integrated approach not only improves operational efficiency but also fosters a competitive advantage in rapidly evolving markets. The model can be used as a conceptual foundation for forming an innovative strategy for sustainable enterprise development amid digital transformation and a dynamic environment.

This case study profiles the steps taken to develop a process for developing a transportation information management system protype. The case study was initiated in response to a pattern of growth whereas, Tanzania is increasingly becoming a mobile society that suffers severely from traffic congestion, delay and mobile source pollution. Meeting the challenges of greater mobility, accessibility, land use development, economic growth and revenue constraints requires that transport investment and maintenance strategies be optimized. In this case study we define transportation information management system (TIMS), in the context of the Tanzania 2014 transportation network. In doing so an argument is made for developing a base line digital transport network analysis, as a starting point. In doing so, we identify a suite of decision-making tools and a stable of performance variables. As well, this case study presents a draft: work plan which emerged during this effort. Developing effective congestion relief strategies requires going beyond the collection of geotechnical like shoulder width, guardrails, vegetation, road length and width, culverts, volume, drainage, terrain, rail crossing, number of lanes, bridges locations, and type of terrain. A comprehensive analysis of congestion requires a fully loaded transportation database with the following characteristics: regional, trunk line, and local roads; travel, time, ADT, population, employment, landuse, and income data at the regional and ward level and; transit line and multi modal infrastructure data. To test multiple congestion mitigation strategies advanced analysis tools are helpful to analyze the impact that network connectivity, pedestrian flows, population growth, travel demand, landuse, and community have accessibility. Equally important is the need have a skilled professional, and technical workforce that has not only the theoretical knowledge but also the technical training to use buffer, proximity, travel demand and simulation tools to assess the impacts of population concentrations, vehicle travel and goods movement.

The transportation sector is the second largest contributor to human-generated carbon dioxide (CO2) emissions. A key goal of the U.S. Department of Transportation is to implement environmentally sustainable policies that can reduce carbon emissions from transportation sources. Smart growth—characterized by compact, mixed use; greater network connectivity; and environments friendly to alternative modes—may encourage reductions in vehicle travel and emissions. A better understanding of travel behavior in conventional and smart growth communities is needed to inform policies. A behavioral data set is analyzed to determine whether smart growth developments are associated with lower CO2 emissions. Sample selection models are estimated from a 2009 travel behavior survey of 15,213 households to capture the conditionality of emissions on the decision to drive (or not) by household members on a given day. Results indicated that 12% of responding households used alternative modes or did not travel from home; the rest of the sample traveled in an automobile and therefore contributed to CO2 emissions. CO2 emissions were calculated from vehicle miles traveled and the fuel efficiency of the vehicle used for specific trips taken by household members. The developed framework models whether CO2 emissions are associated with land use, sociodemographics, and preferences for adopting information technology. Tailpipe CO2 emissions are lower for households that reside in mixed land use neighborhoods with good network connections (on the order of 9%). As a long-term strategy, CO2 emissions reductions from smart growth developments can be substantial.

This review explores the factors influencing the effectiveness of digital learning platforms (DLPs) in enhancing student engagement and academic performance. It identifies key factors such as technological infrastructure, the role of instructors, and student characteristics, which significantly impact the effectiveness of DLPs. Technological infrastructure, including platform availability, network connectivity, and device requirements, plays a crucial role in ensuring accessibility and usability. The instructor's role in adopting active teaching strategies and providing personalized support is also essential for maximizing student success in digital learning environments. Furthermore, student characteristics such as prior knowledge, learning preferences, and technological proficiency influence their ability to engage with and benefit from DLPs. The review presents evidence from empirical studies, case studies, and meta-analyses that demonstrate the positive impact of digital platforms on student engagement and academic outcomes, particularly in blended learning environments. The discussion emphasizes the importance of personalized learning and the need for further research in areas such as cross-cultural comparisons, long-term effectiveness, and equity in access to technology. Recommendations for higher education institutions to optimize the use of DLPs and ensure equitable access for all students are also provided.