Network Links Research Articles

The 40S ribosomal subunit recycling complex modulates mitochondrial dynamics and endoplasmic reticulum - mitochondria tethering at mitochondrial fission/fusion hotspots

The 40S ribosomal subunit recycling pathway is an integral link in the cellular quality control network, occurring after translational errors have been corrected by the ribosome-associated quality control (RQC) machinery. Despite our understanding of its role, the impact of translation quality control on cellular metabolism remains poorly understood. Here, we reveal a conserved role of the 40S ribosomal subunit recycling (USP10-G3BP1) complex in regulating mitochondrial dynamics and function. The complex binds to fission-fusion proteins located at mitochondrial hotspots, regulating the functional assembly of endoplasmic reticulum-mitochondria contact sites (ERMCSs). Furthermore, it alters the activity of mTORC1/2 pathways, suggesting a link between quality control and energy fluctuations. Effective communication is essential for resolving proteostasis-related stresses. Our study illustrates that the USP10-G3BP1 complex acts as a hub that interacts with various pathways to adapt to environmental stimuli promptly. It advances our molecular understanding of RQC regulation and helps explain the pathogenesis of human proteostasis and mitochondrial dysfunction diseases.

Solution Algorithms for the Capacitated Location Tree Problem with Interconnections

This paper addresses the Capacitated Location Tree Problem with Interconnections, a new combinatorial optimization problem with applications in network design. In this problem, the required facilities picked from a set of potential facilities must be opened to serve customers using a tree-shaped network. Costs and capacities are associated with the opening of facilities and the establishment of network links. Customers have a given demand that must be satisfied while respecting the facilities and link capacities. The problem aims to minimize the total cost of designing a distribution network while considering facility opening costs, demand satisfaction, capacity constraints, and the creation of interconnections to enhance network resilience. A valid mixed-integer programming was proposed and an exact solution method based on the formulation was used to solve small- and medium-sized instances. To solve larger instances two metaheuristic approaches were used. A specific decoder procedure for the metaheuristic solution approaches was also proposed and used to help find solutions, especially for large instances. Computational experiments and results using the three solution approaches are also presented. Finally, a case study on the design of electrical transportation systems was presented and solved.

The Structure of Bit-String Similarity Networks.

We study the structural properties of networks formed by random sets of bit strings-namely the ordered arrays of binary variables representing, for instance, genetic information or cultural profiles. Two bit strings are connected by a network link when they are sufficiently similar to each other, i.e., when their Hamming distance is below a certain threshold. Using both analytical and numerical techniques, we determine the degree distribution and the conditions for the existence of a giant component in this kind of network. In addition, we analyze their clustering, assortativity, and mean geodesic distance. We show that these properties combine features specific to random networks with characteristics that derive from the Hamming metrics implicit in the definition of similarity between bit strings.

A Two-Stage Iteration Method for Solving the Departure Time Choice Problem

We discuss the numerical solution of the departure time choice problem. The non–quasi-monotone of the travel cost vector function is first proved to address the study motivation. A two-stage iteration method is then proposed to effectively solve the problem in a single origin-destination (OD) pair network with parallel links, in which departure time and route choices of commuters are involved. We analytically reveal why the iteration method can solve the problem and theoretically prove the convergence, that is, the iteration process finally achieves at a user equilibrium (UE) state. The iteration method is then extended to a single link network with heterogeneous users in the values of travel time and schedule delay and the preferred arrival time. Furthermore, numerical analyses are conducted for the two networks to demonstrate the effectiveness of the iteration method for solving the departure time choice problem. Funding: This work was supported by the National Natural Science Foundation of China [Grants 72288101, 72171007, and 72021001]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2024.0599 .

High-Throughput Secure Multiparty Computation with an Honest Majority in Various Network Settings

In this work, we present novel protocols over rings for semi-honest secure three-party computation (3PC) and malicious four-party computation (4PC) with one corruption. While most existing works focus on improving total communication complexity, challenges such as network heterogeneity and computational complexity, which impact MPC performance in practice, remain underexplored. Our protocols address these issues by tolerating multiple arbitrarily weak network links between parties without any substantial decrease in performance. Additionally, they significantly reduce computational complexity by requiring up to half the number of basic instructions per gate compared to related work. These improvements lead to up to twice the throughput of state-of-the-art protocols in homogeneous network settings and up to eight times higher throughput in real-world heterogeneous settings. These advantages come at no additional cost: Our protocols maintain the best-known total communication complexity per multiplication, requiring 3 elements for 3PC and 5 elements for 4PC.We implemented our protocols alongside several state-of-the-art protocols (Replicated 3PC, ASTRA, Fantastic Four, Tetrad) in a novel open-source C++ framework optimized for high throughput. Five out of six implemented 3PC and 4PC protocols achieve more than one billion 32-bit multiplications or over 32 billion AND gates per second using our implementation in a 25 Gbit/s LAN environment. This represents the highest throughput achieved in 3PC and 4PC so far, outperforming existing frameworks like MP-SPDZ, ABY3, MPyC, and MOTION by two to three orders of magnitude.

ETUDE DE PERFORMANCE DUN NOUVEL ALGORITHME DE LOFDM ADAPTATIF POUR LA MONTEE EN DEBIT DANS LES RESEAUX DACCES OPTIQUES

Orthogonal Frequency Division Multiplexing (OFDM) has demonstrated its effectiveness as a modulation technique in optical communication systems, where data is transmitted and received through optical channels. This paper proposes and implements a novel adaptive OFDM method, called Minimization E-Tighted (MET), aimed at enhancing the data rate in low-cost optical access network links. The implementation was carried out for a Time Division Multiplexing Passive Optical Network (TDM-PON) link, co-simulated using VPIphotonics™ and MATLAB. Simulation results showed that for the simulated target BER values (10⁻³ and 10⁻⁴), the data rates achieved with the MET method are nearly identical to those obtained using the Levin-Campello method, which is commonly employed for rate enhancement. With the simulated parameters at a target BER of 2.23×10⁻⁴, it is demonstrated that 12 Gb/s data rate are achievable on a TDM-PON link with the MET method, supporting 64 users over a 60 km fiber span. Moreover, the MET method proved its capability to optimize the data rate for any given target BER value. Finally, the study revealed that employing high-resolution DAC/ADC converters can further increase the links data rate when the MET algorithm is used.

Predictive Forwarding Rule Caching for Latency Reduction in Dynamic SDN

In mission-critical environments such as industrial and military settings, the use of unmanned vehicles is on the rise. These scenarios typically involve a ground control system (GCS) and nodes such as unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs). The GCS and nodes exchange different types of information, including control data that direct unmanned vehicle movements and sensor data that capture real-world environmental conditions. The GCS and nodes communicate wirelessly, leading to loss or delays in control and sensor data. Minimizing these issues is crucial to ensure nodes operate as intended over wireless links. In dynamic networks, distributed path calculation methods lead to increased network traffic, as each node independently exchanges control messages to discover new routes. This heightened traffic results in internal interference, causing communication delays and data loss. In contrast, software-defined networking (SDN) offers a centralized approach by calculating paths for all nodes from a single point, reducing network traffic. However, shifting from a distributed to a centralized approach with SDN does not inherently guarantee faster route creation. The speed of generating new routes remains independent of whether the approach is centralized, so SDN does not always lead to faster results. Therefore, a key challenge remains: determining how to create new routes as quickly as possible even within an SDN framework. This paper introduces a caching technique for forwarding rules based on predicted link states in SDN, which was named the CRIMSON (Cashing Routing Information in Mobile SDN Network) algorithm. The CRIMSON algorithm detects network link state changes caused by node mobility and caches new forwarding rules based on predicted topology changes. We validated that the CRIMSON algorithm consistently reduces end-to-end latency by an average of 88.96% and 59.49% compared to conventional reactive and proactive modes, respectively.

Optimizing Geo-Distributed Data Processing with Resource Heterogeneity over the Internet

The traditional MapReduce frameworks were originally designed for processing data within a single cluster and are not suitable for handling geo-distributed data. Consequently, alternative approaches such as Hierarchical and Geo-Hadoop have been proposed to address this limitation. However, these approaches still face challenges in efficiently managing inter-cluster data transfer, particularly considering the heterogeneity of clusters and varying bandwidth among them. Moreover, the need to transmit results to a central global reducer for geo-distributed MapReduce operations adds unnecessary complexity. To tackle these issues, we introduce Extended Cross-MapReduce (ECMR), a framework that integrates resource heterogeneity and network links in geo-distributed MapReduce workflows. ECMR optimizes data management and determines the necessary data volume for generating final results. To enhance performance, ECMR leverages the overlap between data transfer and execution time by utilizing multiple global reducers and grouping temporary results that require data transfer over the Internet. In ECMR, we propose a bipartite graph and extend the Gale-Shapley algorithm to determine the optimal number of clusters and select the most suitable locations for global reducers. Through extensive experimental evaluations conducted on a real testbed, we demonstrate the effectiveness of our proposed ECMR method. The results exhibit significant improvements over traditional Hierarchical and Geo-Hadoop approaches, achieving reductions of up to 81% and 85% in overall makespan, respectively.

Synthetic graphs for link prediction benchmarking

Abstract Predicting missing links in complex networks requires algorithms that are able to explore statistical regularities in the existing data. Here we investigate the interplay between algorithm efficiency and network structures through the introduction of suitably-designed synthetic graphs. We propose a family of random graphs that incorporates both micro-scale motifs and meso-scale communities, two ubiquitous
structures in complex networks. A key contribution is the derivation of theoretical upper bounds for link prediction performance in our synthetic graphs, allowing us to estimate the predictability of the task and obtain an improved assessment of the performance of any method. Our results on the performance of classical methods (e.g., Stochastic Block Models, Node2Vec, GraphSage) show that the performance of all methods correlate with the theoretical predictability, that no single method is universally superior, and that each of the methods exploit different characteristics known to exist in large classes of networks. Our findings underline the need for careful consideration of graph structure when selecting a link prediction method and emphasize the value of comparing performance against synthetic benchmarks. We provide open-source code for generating these synthetic graphs, enabling further research on link
prediction methods.

Nonblocking conditions for Clos fabrics with non-uniform switch radixes

Datacenter networks (DCNs) evolve over years and so comprise switches from different generations. Thus, each stage/layer of the Clos fabric may consist of switches with varying radixes (i.e., different port counts), leading to non-uniform stages. While optical circuit switches are increasingly deployed in DCNs to enhance transmission capacity and energy efficiency, the nonblocking condition, crucial for determining the performance of circuit-switched networks, has been established only for Clos fabrics with uniform stages. This study extends the nonblocking condition to Clos fabrics with non-uniform stages. To facilitate practicality, we formulate the condition using integer linear programming (ILP). Using our novel, to our knowledge, condition, we quantitatively demonstrate how much the nonblocking property is compromised under two practical scenarios, random link failures and network expansion, which would break network uniformity. In particular, we reveal that network expansion, common in DCN evolution, could significantly undermine the nonblocking property. Additionally, we assess the computational efficiency of our ILP formulation, which can successfully evaluate the nonblocking property of a large Clos fabric accommodating 32K terminals/uplinks in just 19 min.

Determinants of last-mile travel mode choice under different COVID-19 alert levels: A case study of Batasan Hills, Quezon City, Philippines

The COVID-19 outbreak has led to remarkable changes in the transport sector and people’s travel behavior. The suspension of public transport leads to an increase in the number of private car users and the number of walking activities. The last mile, being one of the weakest links in the transport network, has become more challenging to manage with the imposition of different travel restrictions. Using the data collected from the households of Barangay Batasan Hills, Quezon City, Philippines, this study aimed to understand people’s travel behavior during the pandemic. Specifically, a binary logit model was used to determine the significant factors that affect the last-mile travel mode choice under different alert levels. Results showed that age during the pandemic, monthly household income, the purpose of travel, travel expense, travel time, departure time, origin, compliance with COVID-19 measures, and trip duration have significant factors in last-mile travel mode choice. In addition, risk perception on public transport was also a determinant of last-mile travel mode under alert levels 1 and 2. Analyzing travel behavior during the COVID-19 pandemic is deemed beneficial in devising strategies and interventions that will help mitigate the spread of the virus while still allowing economic activity and the movement of people to happen.

Routing Algorithm Within the Multiple Non-Overlapping Paths' Approach for Quantum Key Distribution Networks.

We develop a novel key routing algorithm for quantum key distribution (QKD) networks that utilizes a distribution of keys between remote nodes, i.e., not directly connected by a QKD link, through multiple non-overlapping paths. This approach focuses on the security of a QKD network by minimizing potential vulnerabilities associated with individual trusted nodes. The algorithm ensures a balanced allocation of the workload across the QKD network links, while aiming for the target key generation rate between directly connected and remote nodes. We present the results of testing the algorithm on two QKD network models consisting of 6 and 10 nodes. The testing demonstrates the ability of the algorithm to distribute secure keys among the nodes of the network in an all-to-all manner, ensuring that the information-theoretic security of the keys between remote nodes is maintained even when one of the trusted nodes is compromised. These results highlight the potential of the algorithm to improve the performance of QKD networks.

Spatiotemporal changes in the congestion index of streets and roads in the armed conflict conditions

This article examines the impact of war on the formation of urban transport flows. During armed conflicts, the transport infrastructure of cities undergoes significant changes, which greatly affects the mobility and safety of the population. The need to study this issue is particularly relevant in the context of the ongoing Russian-Ukrainian war, which has caused the largest migration in Europe since World War II. The paper explores the dynamics of these changes and ways to adapt urban transportation systems to war conditions. The study aims to determine the parameters of urban transport zones with specific disruptions in network link congestion indices during different phases of the full-scale invasion of Ukraine by the Russian Federation. The research methodology is based on analyzing statistical data on population movements, applying traffic flow models, and conducting a systematic analysis of the interaction between various components of urban transport systems. The goal of this study is to establish the relationships between the areas of cities where disruptions in congestion indices were observed during the initial phase of the invasion. The cities studied are Lviv and Kyiv, whose road networks are also described in the article. Polynomial regression models with two independent variables (the congestion index and the number of days from the beginning of each phase) were developed for three predefined time phases, each with distinct features of the armed conflict. The dependent variable is the area of the city experiencing disruptions in the congestion index relative to normal traffic flow conditions. The study concludes that the relationship between changes in the congestion index and the area of the city experiencing deviations is directly proportional. The absolute values of the indicators studied are lower for Lviv’s network than for Kyiv’s.

Towards Deterministic-Delay Data Delivery Using Multi-Criteria Routing over Satellite Networks

The satellite Internet can cover up to 70% of the surface of our planet Earth to provide network services for nearly 3 billion people. As such, it is promising to become the building block of future 6G networks. The satellite Internet is capable of providing uniform communication capacity to every part of the Earth’s surface, due to its uniform and symmetrical constellation structure, while the uneven distribution of ground populations leads to globally uneven traffic delivery requests, incurring a mismatch between the capacity and traffic transmission demands. As such, traditional single-criteria (e.g., shortest delay) routing algorithms can lead to severe network congestion and cannot provision delay-deterministic data delivery. To overcome this bottleneck, we propose a multi-criteria routing and scheduling scheme to redirect time-tolerant data, thus preventing congestion for time-sensitive data, based on the spatiotemporal distribution of data traffic. First, we construct a traffic spatiotemporal distribution model, to indicate the network load status. Next, we model the satellite network multi-criteria routing problem as an integer linear programming one, which is NP-hard and challenging to solve within polynomial time. A novel link weight design based on both the link delay and load is introduced, transforming the mathematical programming problem into a routing optimization problem. The proposed correlation scheduling algorithm fully utilizes idle network link resources, significantly improving network resource utilization and eliminating resource competition between non-time-sensitive and time-sensitive services. Simulation results show that compared with traditional algorithms, the proposed method can increase the throughput of time-sensitive data by up to 20.8% and reduce the packet loss rate of time-sensitive services by up to 76.8%.

The Spatial Network Characteristics of the Coupling Coordination Degree Between Pollution and Carbon Reduction and High-Quality Development in the Yangtze River Economic Belt

Under the requirements of the “double carbon” goal and the new development concept of high-quality development, the coordinated promotion of pollution, carbon reduction, and high-quality development is significant in promoting the social economy’s comprehensive green transformation. The coupling and coordination degree between pollution, carbon reduction, and high-quality development of 110 cities in the Yangtze River Economic Belt from 2010 to 2021 was measured, and the modified gravity model and social network analysis were then employed to assess the spatial network characteristics, patterns, and structure evolution. The results indicated that the coupling and coordination of urban clusters in the Yangtze River Economic Belt showed an increasing pattern, and the spatial arrangement shifted from dispersed to centralized. The spatial linkage strength and potential increased, while the spatial linkage pattern was steady. The direction of spatial connectivity remained consistent, with significant central city orientation and geographical proximity. The overall network structure was characterized by increased network density and an increased number of network links within each urban cluster, while network effectiveness fluctuated and decreased. The predominance of central or developed cities characterized individual network characteristics. This study is of great significance for exploring the coordinated development of pollution reduction, carbon reduction, and high-quality development, providing a theoretical basis for implementing ecological civilization, promoting carbon neutrality, promoting regional coordinated development, and achieving green, low-carbon, and sustainable development.

TD-GCN: A novel fusion method for network topological and dynamical features

The topological and dynamical features of complex networks hold abundant information. How to fully utilize this information for more accurate network structure mining is a significant issue. In this paper, we propose a novel method that simultaneously takes into account both network topological and dynamical features via graph convolutional networks (TD-GCN). Specifically, we obtain the topological features of the network by using the second-order adjacency matrix of the complex network, which captures indirect connections between nodes, for a more detailed representation of network structure, and use the SIS model to generate node state data in the complex network as the dynamical features of the network. The network topological and dynamical features are fused through the graph convolutional neural network. To verify the effectiveness and applicability of our method, we conduct extensive experiments on both simulated networks and real-world networks with various network scales. We comprehensively compare the proposed method with other existing methods in the domains of network link prediction and network node ranking learning. The experimental results show that our method can better capture the characteristic information in complex networks and has better performance compared with other methods.

An unconstrained optimization approach to the stochastic traffic assignment with electric vehicles

This paper develops a model for the joint assignment of traffic and energy demand over a network. The vehicles circulating in the network are partly conventional and partly electric. Some of the network links are equipped with electric charging stations. A stochastic model to represent the drivers’ choices is adopted, and Stochastic User Equilibrium (SUE) conditions are used to obtain the steady-state traffic and energy demand assignment. An unconstrained optimization problem is introduced, and it is proven that its solution is equivalent to imposing the set of SUE conditions without any limitation as regards the model representing the user choice function. A real case study related to a tourist area in the Liguria region (from Genoa airport to Portofino) is treated in detail and a further case study is considered to demonstrate the wide applicability of the approach.

Uncertainty Modeling and Stability Assessment of Minimum Spanning Trees in Network Design

The Minimum Spanning Tree (MST) problem in networks focuses on finding efficient routes, with applications in transportation, logistics, telecommunications, and more. However, catastrophes can make these networks uncertain, requiring robust computational models for decision-making. This paper introduces an uncertainty theory-based model to analyze the stability of MSTs in uncertain networks. By incorporating reliability and risk variables, we assess the robustness of uncertain MSTs (UMSTs) and address the challenge of computing link tolerances, which define the range within which network links can vary without compromising MST optimality. This study proposes computational formulations to systematically calculate these tolerances, offering a more efficient alternative to traditional re-optimization methods.

Brazilian academic mobility: exploring the journey towards training

The exodus of individuals for various reasons or circumstances has increased significantly in recent years in Brazil and the world. One of the reasons identified for this migratory flow is the academic training of these individuals, who seek to train in better quality educational institutions. In this context, this work aims to analyze how the scientific exodus occurs in Brazil, in which individuals from different locations in the country migrate in search of better academic training. Therefore, in this study, we analyzed how the Brazilian scientific exodus occurs. To do this, it was necessary to extract academic training data from the CVs of individuals registered on the Lattes Platform. Therefore, all individuals with completed doctorates were selected, aggregating 381,463 CVs. The choice of this group is justified because it is the group with the highest level of completed academic training, and which has the characteristic of having recently updated data in their CVs. Initially, the data was filtered, selecting the attributes relevant to the research, and finally, the data was processed with the aim of finding the geographic location of the institutions in which the individuals trained. As initial results, it was possible to characterize the data collected on the Lattes Platform, measuring distances traveled by individuals throughout their academic training, as well as flows followed by doctors at state level and an analysis of the internationalization process of doctors Brazilians. Link networks were created demonstrating the main locations occupied by doctors during their academic training, as well as the connections between these locations. Possible indicators were subsequently extracted and analyzed, seeking justifications for the choice of individuals to migrate to locations during training, such as a correlation between the results found and some indicators extracted from open access data repositories, such as population and undergraduate and postgraduate courses. -graduation. Therefore, it was possible to present an unprecedented portrait of how the Brazilian scientific exodus occurs.

Dynamic reinforcement identification method for weak links in distribution network based on deep graph neural network

Abstract Aiming at the insufficient recognition accuracy of weak points in the distribution network, a dynamic enhancement recognition method for weak nodes in the distribution network based on a deep graph neural network is proposed. It integrates the complex topology of the distribution network, automatically extracts key features, and accurately locates and reinforces weak links in real time. The results show that the recognition accuracy and system stability are significantly improved.