Serving the energy demand with renewable energy is hindered by its limited availability near load centres (i.e. places where the energy demand is high). To address this challenge, the concept of Remote Renewable Energy Hubs (RREH) emerges as a promising solution. RREHs are energy hubs located in areas with abundant renewable energy sources, such as sun in the Sahara Desert or wind in Greenland. In these hubs, renewable energy sources are used to synthetise energy molecules. To produce specific energy molecules, a tailored hub configuration must be designed, which means choosing a set of technologies that are interacting with each other as well as defining how they are integrated in their local environment. The plurality of technologies that may be employed in RREHs results in a large diversity of hubs. In order to characterize this diversity, we propose in this paper a taxonomy for accurately defining these hubs. This taxonomy allows to better describe and compare designs of hubs as well as to identify new ones. Thus, it may guide policymakers and engineers in hub design, contributing to cost efficiency and/or improving local integration.
Hub location problems are central to optimizing logistics, telecommunications, and transportation networks by consolidating flows through strategically placed hubs. While existing models assume symmetric allocation, where hubs handle incoming and outgoing flows uniformly, real-world applications often require asymmetric handling of origins and destinations. This paper introduces the Asymmetric Hub Location Problem ((r,s)-AHLP), a novel framework where origins and destinations may connect to hubs under distinct allocation limits (r and s, respectively). We then focus on the (1,p)-AHLP variant, where origins are single-assigned and destinations are multi-assigned, motivated by applications in humanitarian logistics and global supply chains (e.g., UN relief networks, e-commerce fulfillment). We propose two integer programming formulations: A four-index adaptation of classical models and a new compact three-index formulation. The latter reduces the size while improving effectiveness, supported by valid inequalities and decomposition techniques. The computational study, performed on standard datasets commonly used in hub location literature, demonstrates the high effectiveness and effic
Graphical models are popular tools for exploring relationships among a set of variables. The Gaussian graphical model (GGM) is an important class of graphical models, where the conditional dependence among variables is represented by nodes and edges in a graph. In many real applications, we are interested in detecting hubs in graphical models, which refer to nodes with a significant higher degree of connectivity compared to non-hub nodes. A typical strategy for hub detection consists of estimating the graphical model, and then using the estimated graph to identify hubs. Despite its simplicity, the success of this strategy relies on the accuracy of the estimated graph. In this paper, we directly target on the estimation of hubs, without the need of estimating the graph. We establish a novel connection between the presence of hubs in a graphical model, and the spectral decomposition of the underlying covariance matrix. Based on this connection, we propose the method of inverse principal components for hub detection (IPC-HD). Both consistency and convergence rates are established for IPC-HD. Our simulation study demonstrates the superior performance and fast computation of the propose
As cities grapple with traffic congestion and service inequities, mobility hubs offer a scalable solution to align increasing travel demand with sustainability goals. However, evaluating their impacts remains challenging due to the lack of behavioral models that integrate large-scale travel patterns with real-world hub usage. This study presents a novel data fusion approach that incorporates observed mobility hub usage into a mode choice model estimated with synthetic trip data. We identify trips potentially affected by mobility hubs and construct a multimodal sub-choice set, then calibrate hub-specific parameters using on-site survey data and ground truth trip counts. The enhanced model is used to evaluate mobility hub impacts on potential demand, mode shift, reduced vehicle miles traveled (VMT), and increased consumer surplus (CS). We apply this method to a case study in the Capital District, NY, using data from a survey conducted by the Capital District Transportation Authority (CDTA) and a mode choice model estimated using Replica Inc. synthetic data. The two implemented hubs located near UAlbany Downtown Campus and in Downtown Cohoes are projected to generate 8.83 and 6.17 mul
This work studies the effect of hub congestion and time-sensitive demand on a hub-and-spoke location/allocation system. The Hub Location with Congestion and Time-sensitive Demand Problem is introduced, which combines these two main characteristics. On the one hand, hubs can be activated at several service levels, each of them characterized by a maximum capacity, expressed as the amount of flow that may circulate through the hub, which is associated with a hub transit time. On the other hand, alternative levels are available for served commodities, where each demand level is characterized by its amount of demand, unit revenue, and maximum service time. In this problem the efficiency of a hub-and-spoke system is given by the maximum net profit it may produce. To the best of our knowledge this is the first work where hub congestion and time-sensitive demand are jointly considered. Two alternative mixed-integer linear programming formulations are proposed. They include a new set of constraints, which are necessary to guarantee the consistency of the obtained solutions under the presence of the capacity-type constraints derived from hub service levels and served demand levels. The effic
The hubness problem, in which hub embeddings are close to many unrelated examples, occurs often in high-dimensional embedding spaces and may pose a practical threat for purposes such as information retrieval and automatic evaluation metrics. In particular, since cross-modal similarity between text and images cannot be calculated by direct comparisons, such as string matching, cross-modal encoders that project different modalities into a shared space are helpful for various cross-modal applications, and thus, the existence of hubs may pose practical threats. To reveal the vulnerabilities of cross-modal encoders, we propose a method for identifying the hub embedding and its corresponding hub text. Experiments on image captioning evaluation in MSCOCO and nocaps along with image-to-text retrieval tasks in MSCOCO and Flickr30k showed that our method can identify a single hub text that unreasonably achieves comparable or higher similarity scores than human-written reference captions in many images, thereby revealing the vulnerabilities in cross-modal encoders.
Modern logistics systems worldwide are facing unprecedented challenges due to the explosive growth of e-commerce, driving the need for resilient systems to tackle problems such as vulnerable supplies, volatile demands, and fragile transportation networks. Motivated by the innovative concept of the Physical Internet, this paper focuses on resilient capacity deployment of open-access logistics hubs in hyperconnected transportation under demand uncertainty and geographical disruptions. We propose a two-stage stochastic optimization model, aiming to smartly deploy the hub capacity to achieve delivery timeliness, high consolidation and network resilience while minimizing hub set-up budget and truck fleet cost. Four optimal hub network configurations are derived by applying scenarios at four stress testing levels into the optimization model, including deterministic demands without hub disruptions, deterministic demands with hub disruptions, stochastic demands without hub disruptions as well as stochastic demands with hub disruptions. To test the performances of different optimal networks, a simulation-based study is then performed over an automotive delivery-to-dealer network and dataset
A recent approach to the design of flexible electronic devices consists of cutting a two-dimensional sheet to form a central hub connected to several tapered `spokes', resembling the hub-and-spoke of a bicycle wheel. When radially compressed, the resulting cut sheet buckles out-of-plane forming a structure whose three-dimensional shape can be chosen by designing the tapering of the spokes. While the deformation of the spokes in this `hub-and-spoke' kirigami are approximately cylindrical (i.e.~zero Gaussian curvature and hence small elastic strain), this is not the case in the central hub. The central hub is deformed radially because of continuity with the spokes but, because of its own circular symmetry, it must develop Gaussian curvature, and hence strain. In this article we quantify this strain, focussing in particular on its magnitude and its location. We find that the strain is localized in a boundary layer near the edge of the hub region, whose size is controlled by the moment applied on it by the deformed spokes. We discuss the implications of our results for avoiding material failure in flexible-electronic devices.
Statistical network analysis primarily focuses on inferring the parameters of an observed network. In many applications, especially in the social sciences, the observed data is the groups formed by individual subjects. In these applications, the network is itself a parameter of a statistical model. Zhao and Weko (2019) propose a model-based approach, called the hub model, to infer implicit networks from grouping behavior. The hub model assumes that each member of the group is brought together by a member of the group called the hub. The set of members which can serve as a hub is called the hub set. The hub model belongs to the family of Bernoulli mixture models. Identifiability of Bernoulli mixture model parameters is a notoriously difficult problem. This paper proves identifiability of the hub model parameters and estimation consistency under mild conditions. Furthermore, this paper generalizes the hub model by introducing a model component that allows hubless groups in which individual nodes spontaneously appear independent of any other individual. We refer to this additional component as the null component. The new model bridges the gap between the hub model and the degenerate c
Over the past two decades, tools from network science have been leveraged to characterize the organization of both structural and functional networks of the brain. One such measure of network organization is hub node identification. Hubs are specialized nodes within a network that link distinct brain units corresponding to specialized functional processes. Conventional methods for identifying hub nodes utilize different types of centrality measures and participation coefficient to profile various aspects of nodal importance. These methods solely rely on the functional connectivity networks constructed from functional magnetic resonance imaging (fMRI), ignoring the structure-function coupling in the brain. In this paper, we introduce a graph signal processing (GSP) based hub detection framework that utilizes both the structural connectivity and the functional activation to identify hub nodes. The proposed framework models functional activity as graph signals on the structural connectivity. Hub nodes are then detected based on the premise that hub nodes are sparse, have higher level of activity compared to their neighbors, and the non-hub nodes' activity can be modeled as the output
Hubness is a phenomenon in high-dimensional vector spaces where a point from the natural distribution is unusually close to many other points. This is a well-known problem in information retrieval that causes some items to accidentally (and incorrectly) appear relevant to many queries. In this paper, we investigate how attackers can exploit hubness to turn any image or audio input in a multi-modal retrieval system into an adversarial hub. Adversarial hubs can be used to inject universal adversarial content (e.g., spam) that will be retrieved in response to thousands of different queries, and also for targeted attacks on queries related to specific, attacker-chosen concepts. We present a method for creating adversarial hubs and evaluate the resulting hubs on benchmark multi-modal retrieval datasets and an image-to-image retrieval system implemented by Pinecone, a popular vector database. For example, in text-caption-to-image retrieval, a single adversarial hub, generated using 100 random queries, is retrieved as the top-1 most relevant image for more than 21,000 out of 25,000 test queries (by contrast, the most common natural hub is the top-1 response to only 102 queries), demonstra
Physical contact or proximity is often a necessary condition for the spread of infectious diseases. Common destinations, typically referred to as hubs or points of interest, are arguably the most effective spots for the type of disease spread via airborne transmission. In this work, we model the locations of individuals (agents) and common destinations (hubs) by random spatial point processes in $\mathbb{R}^d$ and focus on disease propagation through agents visiting common hubs. The probability of an agent visiting a hub depends on their distance through a connection function $f$. The system is represented by a random bipartite geometric (RBG) graph. We study the degrees and percolation of the RBG graph for general connection functions. We show that the critical density of hubs for percolation is dictated by the support of the connection function $f$, which reveals the critical role of long-distance travel (or its restrictions) in disease spreading.
Many networked systems require a central authority to enforce a global configuration against local peer influence. We study influence dynamics on finite weighted directed graphs with a distinguished hub node and binary vertex states ('Glory' or 'Gnash'). We give a sharp, local, and efficiently checkable criterion that guarantees global convergence to Glory in a single synchronous update from any initial state. At each non-hub vertex, the incoming weight from the hub must at least match the total incoming weight from all other nodes. Specialising in uniform hub broadcasts, the exact threshold equals the maximum non-hub incoming weight over all vertices, and we prove this threshold is tight. We extend the result to a tau-biased update rule and to asynchronous (Gauss-Seidel) schedules, where a single pass still suffices under the same domination hypothesis. Machine-checked proofs in Coq accompany all theorems.
In a graph $G$, we define a set of vertices to be a \emph{strong hub set} if for any two vertices in $G$, we can find a path between them whose internal vertices are all in this set. We define the \emph{strong hub cover pebbling number} of $G$, denoted by $h_s^*(G)$, to be the smallest $t$ such that for any initial configuration with $t$ pebbles on $G$, we can make some pebbling moves (a pebbling move consists of removing two pebbles from a vertex $v$ and adding one pebble to another vertex adjacent to $v$) so that there is a strong hub set with every vertex in it having a pebble. We determine the strong hub cover pebbling numbers of paths, stars, and books.
Droplet-fiber interactions, prevalent in nature and widely applied across various engineering fields, have garnered significant research interest. Many works have focused on the interactions between droplets and single or two fibers. However, the wetting behavior of droplets, especially the maximum droplets that can be retained, on fiber hubs formed by many fibers is rarely studied. The current work explores the capability of fiber hubs to retain liquid droplets. We develop analytical and semi-empirical models to predict the maximum droplet volume on a fiber hub, validating them against experimental data. The variation of maximum volume follows two distinct regimes as the fiber count increases, with a critical fiber number ($n^* = 32$) marking the transition between them. In Regime I ($n\le n^*$), the volume increases with fiber number, and the stability of a droplet is dictated by the pinning of three-phase contact lines. In Regime II ($n>n^*$), the volume plateaus, with droplets under a fiber hub behaving similarly to those on a flat surface, where the stability is governed by Rayleigh-Taylor instability.
Many industries widely adopted hub networks these days. Managing logistics, transportation, and distribution requires a delicate balance to ensure seamless operations within this network. Hub network promotes cost-effective routing through inter-hub flows. Failure of such hubs may impact the total network with huge costs. In this paper, we study bilevel $q$-allocation hub interdiction problem. In previous literature hub interdiction problem was studied with single and multiple allocation protocols.This problem focus on $q$ allocation which solves single and multiple allocation problem as special case. We also show improvements on the branch and cut approach by using cutting planes.Our experiments based on large instances present the efficiency of the approach to solve some previously unsolved instances.
Hub Covering Problems arise in various practical domains, such as urban planning, cargo delivery systems, airline networks, telecommunication network design, and e-mobility. The task is to select a set of hubs that enable tours between designated origin-destination pairs while ensuring that any tour includes no more than two hubs and that either the overall tour length or the longest individual edge is kept within prescribed limits. In literature, three primary variants of this problem are distinguished by their specific constraints. Each version exists in a single and multi allocation version, resulting in multiple distinct problem statements. Furthermore, the capacitated versions of these problems introduce additional restrictions on the maximum number of hubs that can be opened. It is currently unclear whether some variants are more complex than others, and no approximation bound is known. In this paper, we establish a hierarchy among these problems, demonstrating that certain variants are indeed special cases of others. For each problem, we either determine the absence of any approximation bound or provide both upper and lower bounds on the approximation guarantee.
The multi allocation p-hub median problem (MApHM), the multi allocation uncapacitated hub location problem (MAuHLP) and the multi allocation p-hub location problem (MApHLP) are common hub location problems with several practical applications. HLPs aim to construct a network for routing tasks between different locations. Specifically, a set of hubs must be chosen and each routing must be performed using one or two hubs as stopovers. The costs between two hubs are discounted. The objective is to minimize the total transportation cost in the MApHM and additionally to minimize the set-up costs for the hubs in the MAuHLP and MApHLP. In this paper, an approximation algorithm to solve these problems is developed, which improves the approximation bound for MApHM to 3.451, for MAuHLP to 2.173 and for MApHLP to 4.552 when combined with the algorithm of Benedito & Pedrosa. The proposed algorithm is capable of solving much bigger instances than any exact algorithm in the literature. New benchmark instances have been created and published for evaluation, such that HLP algorithms can be tested and compared on huge instances. The proposed algorithm performs on most instances better than the a
This paper introduces a new formulation and solution framework for hub location problems. The formulation is based on 2-index aggregated flow variables and incorporates a set of aggregated demand constraints, which are novel in hub location. With minor adaptations, the approach applies to a large class of single- and multiple-allocation models, possibly incorporating flow bounds on activated arcs. General-purpose feasibility and optimality inequalities are also developed. Because of the small number of continuous variables, there is no need to project them out, differentiating the method from solution algorithms that rely heavily on feasibility and optimality cuts. The proposed Branch & Solve solution framework leverages the nested structure of the problems, by solving auxiliary subproblems at selected nodes of the enumeration tree. Extensive computational experiments on benchmark instances from the literature confirm the good performance of the proposal: the basic version of the algorithm is able to solve to proven optimality instances with up to 200 nodes for several hub location families.
The COVID-19 pandemic highlighted the urgent need for robust systems to enable rapid data collection, integration, and analysis for public health responses. Existing approaches often relied on disparate, non-interoperable systems, creating bottlenecks in comprehensive analyses and timely decision-making. To address these challenges, the U.S. National Institutes of Health (NIH) launched the Rapid Acceleration of Diagnostics (RADx) initiative in 2020, with the RADx Data Hub, a centralized repository for de-identified and curated COVID-19 data, as its cornerstone. The RADx Data Hub hosts diverse study data, including clinical data, testing results, smart sensor outputs, self-reported symptoms, and information on social determinants of health. Built on cloud infrastructure, the RADx Data Hub integrates metadata standards, interoperable formats, and ontology-based tools to adhere to the FAIR (Findable, Accessible, Interoperable, Reusable) principles for data sharing. Initially developed for COVID-19 research, its architecture and processes are adaptable to other scientific disciplines. This paper provides an overview of the data hosted by the RADx Data Hub and describes the platform's c