Terrorist attacks on transportation networks have traumatized modern societies. With a single blast, it has become possible to paralyze airline traffic, electric power supply, ground transportation or Internet communication. How and at which cost can one restructure the network such that it will become more robust against a malicious attack? We introduce a new measure for robustness and use it to devise a method to mitigate economically and efficiently this risk. We demonstrate its efficiency on the European electricity system and on the Internet as well as on complex networks models. We show that with small changes in the network structure (low cost) the robustness of diverse networks can be improved dramatically whereas their functionality remains unchanged. Our results are useful not only for improving significantly with low cost the robustness of existing infrastructures but also for designing economically robust network systems.
Abstract Smart cities are aimed to efficiently manage growing urbanization, energy consumption, maintain a green environment, improve the economic and living standards of their citizens, and raise the people’s capabilities to efficiently use and adopt the modern information and communication technology (ICT). In the smart cities concept, ICT is playing a vital role in policy design, decision, implementation, and ultimate productive services. The primary objective of this review is to explore the role of artificial intelligence (AI), machine learning (ML), and deep reinforcement learning (DRL) in the evolution of smart cities. The preceding techniques are efficiently used to design optimal policy regarding various smart city-oriented complex problems. In this survey, we present in-depth details of the applications of the prior techniques in intelligent transportation systems (ITSs), cyber-security, energy-efficient utilization of smart grids (SGs), effective use of unmanned aerial vehicles (UAVs) to assure the best services of 5G and beyond 5G (B5G) communications, and smart health care system in a smart city. Finally, we present various research challenges and future research directions where the aforementioned techniques can play an outstanding role to realize the concept of a smart city.
We live in a modern world supported by large, complex networks. Examples range from financial markets to communication and transportation systems. In many realistic situations the flow of physical quantities in the network, as characterized by the loads on nodes, is important. We show that for such networks where loads can redistribute among the nodes, intentional attacks can lead to a cascade of overload failures, which can in turn cause the entire or a substantial part of the network to collapse. This is relevant for real-world networks that possess a highly heterogeneous distribution of loads, such as the Internet and power grids. We demonstrate that the heterogeneity of these networks makes them particularly vulnerable to attacks in that a large-scale cascade may be triggered by disabling a single key node. This brings obvious concerns on the security of such systems.
Abstract In response to the demand for high-frequency ultra-wideband power amplifiers (PAs) in high-speed reliable communication fields such as high-speed railway and highway transportation, a single-chip integrated circuit (MMIC) for ultra-wideband PAs operating in 2–6 GHz based on 0.25 μm GaN process is proposed in this paper. For high-speed communication in transportation applications, the PA achieves the optimal output impedance through the load/source pull method, employs a resistor capacitor (RC) parallel network to enhance stability, and utilizes an RLC/LC bias circuit to ensure the stability performance of the radio frequency (RF) circuit. The paper innovatively proposes a method for optimizing full frequency impedance matching through RC curve fitting and combines electro-magnet (EM) co-simulation to achieve 50 Ω impedance matching, significantly improving design efficiency and accuracy. The measured results show that the GaN MMIC PA has an output power greater than 39 dBm, a power gain greater than 18 dB, and a power-added efficiency (PAE) greater than 37% (peak 48.2%) in the 2–6 GHz frequency band. The overall size of the chip is 1.9 mm × 2.4 mm. This paper can provide guidance for core components of high-speed transportation communication systems, meeting the diverse requirements of high-speed wireless communication in the transportation field for transmitters.
In this study, we investigate the long-term evolution of the hierarchical structure and the stability of centrality correlations within Japan's cross-shareholding networks. Using a 23-year dataset (2001-2023) of all listed Japanese companies, we conducted network analysis of semi-annually constructed networks of industrial sectors based on strongly connected components. To examine hierarchical structures, centrality distributions, and their intercorrelations, we applied bow-tie decomposition, PageRank, and the Hypertext-Induced Topic Selection (HITS) algorithm. The largest strongly connected component (the core group) decreased over time, whereas the number of companies that held shares in core members without being held in return (IN) or that remained entirely disconnected increased. Correlations among network measures exhibited temporal stability. The in-strength was positively correlated with both PageRank (pa), computed from the original directed network, and an authority score, whereas the out-strength was positively correlated with the Reversed PageRank (ph) computed from the reversed network and the hub score. A negative correlation was observed between pa and ph. The correlation between in-strength and out-strength shifted from negative to positive around 2010, suggesting stronger cross-shareholding ties among companies. Most industries exhibited network-measure correlations similar to those observed in the overall network. In contrast, transportation equipment showed no significant correlation between in-strength and the corresponding pa, which suggests that firms were less influenced despite holding significant inbound voting rights. The relative ranking of network measures across industries remained stable over time. Banks consistently ranked low for in-strength and high for out-strength. Although their ph ranks remained high, their hub score ranks decreased. These findings, such as the declining influence of banks and the rising centrality of the information & communication and real estate sectors, suggest that traditional firm-level or short-term monitoring may overlook systemic ownership structures. Periodic network-based monitoring can help to identify resilient and structurally influential firms or clusters.
With the rapid advancement of digital twin-enabled intelligent transportation systems, efficient migration has become essential for maintaining real-time responsiveness and reliability. Existing approaches, however, primarily emphasize resource-aware optimization while neglecting the substantial overhead from state synchronization and redundant data transmission. Moreover, they typically treat digital twins as indivisible entities, overlooking optimization opportunities at the sub-model level. This limitation results in excessive migration costs and suboptimal resource utilization. To overcome these challenges, we propose a fine-grained model-level digital twin migration framework, FGDT, featuring three key components: (i) an explicit-implicit fused coupling graph construction captures both functional dependencies and latent collaborations among heterogeneous sub-models; (ii) a skew-aware migration pattern selection dynamically balances joint versus independent migration, thereby minimizing communication overhead and improving resource allocation; and (iii) a model-level migration strategy optimization strategy leverages dual-network PPO with a soft-constraint co-placement mechanism to support adaptive, fine-grained migration decisions. Extensive experiments validate the effectiveness of FGDT, which significantly reduces average system latency while maintaining low migration overhead, thereby enhancing both resource efficiency and overall system performance.
Transportation engineering, Transportation and communications
Rafael R. Maciel, Adler Diniz de Souza, Rodrigo M. A. Almeida
et al.
<i>Background</i>: Waste collection is a critical logistical challenge in urban management, and while Internet of Things (IoT) technologies are increasingly used to optimize collection routes, a systematic, quantitative synthesis of their impact is lacking. This study aims to bridge this gap by quantifying the effect of IoT-enabled routing optimization on waste collection distances. <i>Methods</i>: We conducted a systematic review and meta-analysis following the PRISMA protocol, searching the Scopus, IEEE Xplore, and ACM Digital Library databases. This process yielded 11 eligible studies, providing 21 distinct samples for quantitative synthesis. <i>Results</i>: The analysis reveals that IoT-enabled routing optimization reduces collection distance by a combined average of 21.51%. A significant disparity was found between study types, with simulation-based approaches reporting higher reductions (−39.79%) compared to real-world deployments (−12.37%). No statistically significant performance differences were observed across different routing algorithm categories or Vehicle Routing Problem (VRP) variants. <i>Conclusions</i>: These findings provide robust quantitative evidence of the significant efficiency gains from implementing IoT-based smart waste management systems. The gap between simulated and real-world results underscores the need for practitioners to set realistic expectations, while our analysis supports the adoption of these technologies for more sustainable urban logistics.
Transportation and communication, Management. Industrial management