ISC - Institute of Smart Cities

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Open Access
An acceleration approach for channel deterministic approaches based on quasi-stationary regions in V2X communications
(IEEE, 2024) Rodríguez Corbo, Fidel Alejandro; Celaya Echarri, Mikel; Shubair, Raed M.; Falcone Lanas, Francisco; Azpilicueta Fernández de las Heras, Leyre; Ingeniería Eléctrica, Electrónica y de Comunicación; Ingeniaritza Elektrikoa, Elektronikoa eta Telekomunikazio Ingeniaritza; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika; Institute of Smart Cities - ISC
Vehicular environments are characterized by a high mobility, which alongside with the presence of abundant dynamic scatterers, lead to vehicular communication channels to be intrinsically non-stationary. In this sense, the quasi-stationary regions (QSRs) can assess the degree of non-stationarity within a determined scenario, and ultimately assist geometrical models to increase channel sampling intervals or to develop more efficient hybrid stochastic-geometric channel models. In this work, the channel QSRs in a vehicular communication (V2X) generic highdense urban environment at millimeter wave (mmWave) frequencies (28 GHz) have been analyzed using different approaches, such as the extended channel response into a Doppler-delay domain or the shadow fading spatial auto-correlation function (SF ACF) methodology. Then, the QSRs have been used as sampling distance in an in-house developed three-dimensional ray-launching (3D-RL) algorithm as an acceleration approach. The time variant channel features have been extracted and compared with the full resolution approach, obtaining consistent results when considering the QSR sampling distances, while decreasing by 83.30% the simulation computational time for the Doppler-delay approach, and 92.86% for the SF ACF method.
Open Access
Acoustic lock: position and orientation trapping of non-spherical sub-wavelength particles in mid-air using a single-axis acoustic levitator
(American Institute of Physics, 2018) Cox, L; Croxford, A; Drinkwater, Bruce W.; Marzo Pérez, Asier; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika; Institute of Smart Cities - ISC
We demonstrate acoustic trapping in both position and orientation of a non-spherical particle of sub-wavelength size in mid-air. To do so, we multiplex in time a pseudo-one-dimensional vertical standing wave and a twin-trap; the vertical standing wave provides converging forces that trap in position, whereas the twin-trap applies a stabilising torque that locks the orientation. The device operates at 40 kHz, and the employed multiplexing ratio of the 2 acoustic fields is 100:50 (standing:twin) periods. This ratio can be changed to provide tunability of the relative trapping strength and converging torque. The torsional spring stiffness of the trap is measured through simulations and experiments with good agreement. Cubes from k/5.56 (1.5 mm) to k/2.5 (3.4 mm) side length were stably locked. We also apply this technique to lock different non-spherical particles in midair: cubes, pyramids, cylinders, and insects such as flies and crickets. This technique adds significant functionality to mid-air acoustic levitation and will enable applications in micro-scale manufacturing as well as containment of specimens for examination and 3D-scanning.
Open Access
Acuity-based rotational patient-to-physician assignment in an emergency department using electronic health records in triage
(SAGE, 2023) Cildoz Esquíroz, Marta; Ibarra Bolt, Amaya; Mallor Giménez, Fermín; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika; Institute of Smart Cities - ISC
Emergency department (ED) operational metrics generated by a new acuity-based rotational patient-to-physician assignment (ARPA) algorithm are compared with those obtained with a simple rotational patient assignment (SRPA) system aimed only at an equitable patient distribution. The new ARPA method theoretically guarantees that no two physicians’ assigned patient loads can differ by more than one, either partially (by acuity levels) or in total; whereas SRPA guarantees only the latter. The performance of the ARPA method was assessed in practice in the ED of the main public hospital (Hospital Compound of Navarra) in the region of Navarre in Spain. This ED attends over 140 000 patients every year. Data analysis was conducted on 9,063 ED patients in the SRPA cohort, and 8,892 ED patients in the ARPA cohort. The metrics of interest are related both to patient access to healthcare and physician workload distribution: patient length of stay; arrival-to-provider time; ratio of patients exceeding the APT target threshold; and range of assigned patients across physicians by priority levels. The transition from SRPA to ARPA is associated with improvements in all ED operational metrics. This research demonstrates that ARPA is a simple and useful strategy for redesigning front-end ED processes.
Open Access
Admissible OWA operators for fuzzy numbers
(Elsevier, 2024) García-Zamora, Diego; Cruz, Anderson; Neres, Fernando; Santiago, Regivan; Roldán López de Hierro, Antonio Francisco; Paiva, Rui; Pereira Dimuro, Graçaliz; Martínez López, Luis; Bedregal, Benjamin; Bustince Sola, Humberto; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika; Institute of Smart Cities - ISC
Ordered Weighted Averaging (OWA) operators are some of the most widely used aggregation functions in classic literature, but their application to fuzzy numbers has been limited due to the complexity of defining a total order in fuzzy contexts. However, the recent notion of admissible order for fuzzy numbers provides an effective method to totally order them by refining a given partial order. Therefore, this paper is devoted to defining OWA operators for fuzzy numbers with respect to admissible orders and investigating their properties. Firstly, we define the OWA operators associated with such admissible orders and then we show their main properties. Afterward, an example is presented to illustrate the applicability of these AOWA operators in linguistic decision-making. In this regard, we also develop an admissible order for trapezoidal fuzzy numbers that can be efficiently applied in practice.
Open Access
Agent-based simulation improves e-grocery deliveries using horizontal cooperation
(IEEE, 2020) Serrano Hernández, Adrián; Faulín Fajardo, Javier; Torre Martínez, Rocío de la; Cadarso, Luis; Estatistika, Informatika eta Matematika; Enpresen Kudeaketa; Institute of Smart Cities - ISC; Institute for Advanced Research in Business and Economics - INARBE; Estadística, Informática y Matemáticas; Gestión de Empresas
E-commerce has increased tremendously in recent decades because of improvements in the information and telecommunications technology along with changes in societal lifestyles. More recently, e-grocery (groceries purchased online) including fresh vegetables and fruit, is gaining importance as the most-efficient delivery system in terms of cost and time. In this respect, we evaluate the effect of cooperation-based policies on service quality among different supermarkets in Pamplona, Spain. Concerning the methodology, we deploy, firstly, a detailed survey in Pamplona in order to model e-grocery demand patterns. Secondly, we develop an agent-based simulation model for generating scenarios in cooperative and non-cooperative settings, considering the real data obtained from the survey analysis. Thus, a Vehicle Routing Problem is dynamically generated and solved within the simulation framework using a biased-randomization algorithm. Finally, the results show significant reductions in lead times and better customer satisfaction when employing horizontal cooperation in e-grocery distribution.
Open Access
Aggregation functions based on the Choquet integral applied to image resizing
(Atlantis Press, 2019) Bueno, Jéssica C. S.; Dias, Camila A.; Pereira Dimuro, Graçaliz; Santos, Helida; Bustince Sola, Humberto; Estatistika, Informatika eta Matematika; Institute of Smart Cities - ISC; Estadística, Informática y Matemáticas
The rising volume of data and its high complexity has brought the need of developing increasingly efficient knowledge extraction techniques, which demands efficiency both in computational cost and in accuracy. Most of problems that are handled by these techniques has complex information to be identified. So, machine learning methods are frequently used, where a variety of functions can be applied in the different steps that are employed in their architecture. One of them is the use of aggregation functions aiming at resizing images. In this context, we introduce a study of aggregation functions based on the Choquet integral, whose main characteristic in comparison with other aggregation functions is that it considers, through fuzzy measure, the interaction between the elements to be aggregated. Thus, our main goal is to present an evaluation study of the performance of the standard Choquet integral the and copula-based generalization of the Choquet integral in relation to the maximum and mean functions, looking for results that may be better than the aggregation functions commonly applied. The results of such comparisons are promising, when evaluated through image quality metrics.
Open Access
Aggregation of deep features for image retrieval based on object detection
(Springer, 2019-09-22) Forcén Carvalho, Juan Ignacio; Pagola Barrio, Miguel; Barrenechea Tartas, Edurne; Bustince Sola, Humberto; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika; Institute of Smart Cities - ISC; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
Image retrieval can be tackled using deep features from pretrained Convolutional Neural Networks (CNN). The feature map from the last convolutional layer of a CNN encodes descriptive information from which a discriminative global descriptor can be obtained. However, this global descriptors combine all of the information of the image, giving equal importance to the background and the object of the query. We propose to use an object detection based on saliency models to identify relevant regions in the image and therefore obtain better image descriptors. We extend our proposal to multi-regional image representation and we combine our proposal with other spatial weighting measures. The descriptors derived from the salient regions improve the performance in three well known image retrieval datasets as we show in the experiments.
Open Access
Aggregator to electric vehicle LoRaWAN based communication analysis in vehicle-to-grid systems in smart cities
(IEEE, 2020) Klaina, Hicham; Picallo Guembe, Imanol; López Iturri, Peio; Astrain Escola, José Javier; Azpilicueta Fernández de las Heras, Leyre; Falcone Lanas, Francisco; Ingeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio Ingeniaritzaren; Estatistika, Informatika eta Matematika; Institute of Smart Cities - ISC; Ingeniería Eléctrica, Electrónica y de Comunicación; Estadística, Informática y Matemáticas
Recently, there has been growing attention to the power grid management due to the increasing concerns on global warming. With the advancement in electric vehicles (EV) industry and the evolution in batteries, EVs become an important contributor to the grid with capability of bidirectional power exchange with the grid. In this context, Vehicle-to-Grid (V2G) systems enable multiple functionalities between EVs and the corresponding aggregator. Thus, reliable, long-range communication capabilities between aggregator and EVs is compulsory. In this paper, wireless channel analysis for aggregator and electrical vehicle communication using Long-Range Wide Area Network (LoRaWAN) technology in V2G is presented, in order to test a low-cost solution with large coverage and reduced power consumption profile. Wireless channel and system-level measurements have been performed in a real urban scenario between EV's charging station in Pamplona (Spain) and a vehicle in motion using LoRaWAN 868 MHz devices. Wireless channel characterization is performed by implementing a full 3D urban scenario model, including elements such as buildings, vehicles, users and urban infrastructure such as lamp posts and benches. By means of in-house developed 3D Ray Launching algorithm with hybrid simulation capabilities, estimations of received power levels, signal to noise ratio and time domain parameters have been obtained, for the complete volume of the scenario under test in dense urban conditions. V2G end to end communication has been validated by implementing an intra-vehicle Controller Area Network-BUS (CAN BUS) data gathering system connected to the vehicle LoRaWAN transceiver and subsequently, to a cloud-based web service. The results show that the accurate deterministic based radio channel analysis enables to optimize the network design of LoRaWAN networks in a vehicular environment, considering inter-vehicular and infrastructure links, enabling scalable, low cost end to end data exchange for the deployment of ancillary V2G services.
Open Access
Análisis de los cambios en los patrones de temperatura mediante técnicas de stream clustering
(CAEPIA, 2024) Urío Larrea, Asier; Pereira Dimuro, Graçaliz; Andreu-Pérez, Javier; Camargo, Heloisa A.; Aguirre Eraso, Javier; Bustince Sola, Humberto; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika; Institute of Smart Cities - ISC; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
El cambio climático afecta a las condiciones medioambientales de las distintas regiones. La capacidad de constatar estos cambios es una eficaz herramienta para adaptarse a la evolución de las condiciones. Los datos meteorológicos se generan continuamente en múltiples estaciones de todo el mundo, proporcionando una valiosa información sobre la variabilidad en el tiempo de los patrones climáticos. El estudio de este flujo de datos nos permite comprender mejor los nuevos patrones climáticos. Este trabajo explora, mediante un algoritmo de agrupamiento de flujos de datos (stream clustering), el potencial de emplear datos meteorológicos obtenidos en diferentes localizaciones geográficas para rastrear el cambio en los patrones climáticos en la Comunidad Foral de Navarra durante los últimos 20 años. El estudio de caso mostró la aplicabilidad de los métodos de flujos de datos a la segmentación incremental de regiones geográficas en función de sus factores climatológicos.
Open Access
Analysing capacity challenges in the Multi-Airport System of Mexico City
(Dime University of Genoa, 2022) Mújica Mota, Miguel; Faulín Fajardo, Javier; Izco Berastegui, Irene; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika; Institute of Smart Cities - ISC
The relentless growth in Mexico City’s aviation traffic has inevitably strained capacity development of its airport, raising the dilemma between the possible solutions. In the present study, Mexico’s Multi-Airport System is subjected to analysis by means of multi-model simulation, focusing on the capacity-demand problem of the system. The methodology combines phases of modelling, data collection, simulation, experimental design, and analysis. Drawing a distinction from previous works involving two-airport systems. It also explores the challenges raised by the Covid-19 pandemic in Mexico City airport operations, with a discrete-event simulation model of a multi-airport system composed by three airports (MEX, TLC, and the new airport NLU). The study is including the latest data of flights, infrastructures, and layout collected in 2021. Therefore, the paper aims to answer to the question of whether the system will be able to cope with the expected demand in a short-, medium-, and long term by simulating three future scenarios based on aviation forecasts. The study reveals potential limitations of the system as time evolves and the feasibility of a joint operation to absorb the demand in such a big region like Mexico City
Open Access
Analysis of inter-train wireless connectivity to enable context aware rail applications
(Springer, 2021) Picallo Guembe, Imanol; López Iturri, Peio; Celaya Echarri, Mikel; Azpilicueta Fernández de las Heras, Leyre; Astrain Escola, José Javier; Falcone Lanas, Francisco; Estadística, Informática y Matemáticas; Ingeniería Eléctrica, Electrónica y de Comunicación; Institute of Smart Cities - ISC; Estatistika, Informatika eta Matematika; Ingeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio Ingeniaritzaren
Train systems are fundamental players within multi-modal transit systems, providing efficient transportation means for passengers and goods. In the framework of Smart Cities and Smart Regions, providing context aware environments is compulsory in order to take full advantage of system integration, with updated information exchange among Intelligent Transportation system deployments. In this work, inter-train wireless system connectivity is analyzed with the aid of deterministic 3D wireless channel approximations, with the aim of obtaining estimations of frequency/power volumetric channel distributions, as well as time domain characteristics, for different frequency bands. The results show the impact of the complex inter-train scenario conditions, which require precise channel modelling in order to perform optimal network design, planning and optimization tasks.
Open Access
Assessing the impact of physicians' behavior variability on performance indicators in emergency departments: an agent-based model
(IEEE, 2025-01-20) Baigorri Iguzquiaguirre, Miguel; Cildoz Esquíroz, Marta; Mallor Giménez, Fermín; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika; Institute of Smart Cities - ISC
In emergency departments (EDs), traditional simulation models often overlook the variability in physician practice, assuming uniform service provision. Our study introduces a hybrid agent-based discrete-event simulation (AB-DES) model to capture this variability. Through simulation scenarios based on real ED data, we assess the impact of physician behavior on key performance indicators such as patient waiting times and physician stress levels. Results show significant variability in both individual physician performance and average metrics across scenarios. By integrating physician agent modeling, informed by literature from medical and workplace psychology, our approach offers a more nuanced representation of ED dynamics. This model serves as a foundation for future developments towards digital twins, facilitating real-time ED management. Our findings emphasize the importance of considering physician behavior for accurate performance assessment and optimization.
Open Access
Bargaining for the last mile cost and environmental preferences of stakeholders: an economic experiment
(Elsevier, 2025-01-09) Denant-Boemont, Laurent; Faulín Fajardo, Javier; Hammiche, Sabrina; Serrano Hernández, Adrián; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika; Institute of Smart Cities - ISC; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
We aim at studying how environmental preferences matter when consumers negotiate with sellers in order to contract for delivery at home. To do that, we build an economic laboratory experiment where pairs of participants bargain for choosing either the click-and-collect option, which is free for consumer but implies for him private transportation costs, or the delivery-at-home option, which is pricey for him, but externalize transportation cost to the seller. In addition, in our game, transportation triggers environmental costs that are borne by both partners. We have 4 different treatments: The first one, as a benchmark, corresponds to an ultimatum bargaining game about the last mile cost with environmental costs. In the second one, we deliver a message about environmental impacts of transportation to the buyer, whereas, in the third one, the same message is delivered to the seller. The last one is a control where the message is delivered to both partners. The preliminary results (which included 178 participants) show that the average delivery price proposed by sellers is below Nash equilibrium price but above the "behavioral price" and that acceptance rates of seller's proposals by buyers are quite high.
Open Access
Cátedra Mujer, Ciencia y Tecnología de la UPNA
(Gobierno de Navarra, 2023) Aranguren Garacochea, Patricia; Barrenechea Tartas, Edurne; Catalán Ros, Leyre; Díaz Lucas, Silvia; Jurío Munárriz, Aránzazu; Martínez Ramírez, Alicia; Millor Muruzábal, Nora; Gómez Fernández, Marisol; San Martín Biurrun, Idoia; Ingeniería Eléctrica, Electrónica y de Comunicación; Ingeniaritza Elektrikoa, Elektronikoa eta Telekomunikazio Ingeniaritza; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika; Ingeniería; Ingeniaritza; Institute of Smart Cities - ISC; Institute for Advanced Materials and Mathematics - INAMAT2
La Cátedra Mujer, Ciencia y Tecnología de la Universidad Pública de Navarra (UPNA) tiene como objetivo aumentar la participación de las mujeres en campos de ciencia y tecnología. La cultura y la divulgación científicas son el eje principal de la actividad de la Cátedra. Dicha actividad engloba: la representación teatral Yo quiero ser científica, talleres experimentales y conferencias y exposiciones para todos los públicos y edades. Más de 6000 personas han visto la obra de teatro, más de 1500 estudiantes de ESO han participado en los talleres y el material audiovisual ha recibido más de 20000 visitas.
Open Access
CFM-BD: a distributed rule induction algorithm for building compact fuzzy models in Big Data classification problems
(IEEE, 2020) Elkano Ilintxeta, Mikel; Sanz Delgado, José Antonio; Barrenechea Tartas, Edurne; Bustince Sola, Humberto; Galar Idoate, Mikel; Estatistika, Informatika eta Matematika; Institute of Smart Cities - ISC; Estadística, Informática y Matemáticas
Interpretability has always been a major concern for fuzzy rule-based classifiers. The usage of human-readable models allows them to explain the reasoning behind their predictions and decisions. However, when it comes to Big Data classification problems, fuzzy rule based classifiers have not been able to maintain the good tradeoff between accuracy and interpretability that has characterized these techniques in non-Big-Data environments. The most accurate methods build models composed of a large number of rules and fuzzy sets that are too complex, while those approaches focusing on interpretability do not provide state-of-the-art discrimination capabilities. In this paper, we propose a new distributed learning algorithm named CFM-BD to construct accurate and compact fuzzy rule-based classification systems for Big Data. This method has been specifically designed from scratch for Big Data problems and does not adapt or extend any existing algorithm. The proposed learning process consists of three stages: Preprocessing based on the probability integral transform theorem; rule induction inspired by CHI-BD and Apriori algorithms; and rule selection by means of a global evolutionary optimization. We conducted a complete empirical study to test the performance of our approach in terms of accuracy, complexity, and runtime. The results obtained were compared and contrasted with four state-of-the-art fuzzy classifiers for Big Data (FBDT, FMDT, Chi-Spark-RS, and CHI-BD). According to this study, CFM-BD is able to provide competitive discrimination capabilities using significantly simpler models composed of a few rules of less than three antecedents, employing five linguistic labels for all variables.
Open Access
Co-occurrence of deep convolutional features for image search
(Elsevier, 2020) Forcén Carvalho, Juan Ignacio; Pagola Barrio, Miguel; Barrenechea Tartas, Edurne; Bustince Sola, Humberto; Estatistika, Informatika eta Matematika; Institute of Smart Cities - ISC; Estadística, Informática y Matemáticas
Image search can be tackled using deep features from pre-trained Convolutional Neural Networks (CNN). The feature map from the last convolutional layer of a CNN encodes descriptive information from which a discriminative global descriptor can be obtained. We propose a new representation of co-occurrences from deep convolutional features to extract additional relevant information from this last convolutional layer. Combining this co-occurrence map with the feature map, we achieve an improved image representation. We present two different methods to get the co-occurrence representation, the first one based on direct aggregation of activations, and the second one, based on a trainable co-occurrence representation. The image descriptors derived from our methodology improve the performance in very well-known image retrieval datasets as we prove in the experiments.
Open Access
Cognitive assistant for physical exercise monitoring in hand rehabilitation
(Springer, 2023-08-21) Rincón Arango, Jaime Andrés; Marco Detchart, Cedric; Julian, Vicente; Carrascosa, Carlos; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika; Institute of Smart Cities - ISC
This paper introduces a novel, affordable companion robot that has been designed for rehabilitation purposes among the elderly population. The robot is equipped with a camera that records exercises, and an animation screen that delivers clear and easy-to-follow instructions and feedback. To evaluate the device, a machine learning algorithm was used on a dataset of therapy exercises. The results indicate that the robot effectively recognizes gestures and accurately identifies the exercises being performed. This study presents a groundbreaking and cost-effective solution for elderly rehabilitation and has the potential to revolutionize the industry with its cutting-edge technology.
Open Access
Comment on 'Effects of Vivifrail multicomponent intervention on functional capacity' by Casas-Herrero et al.-The authors reply.
(Wiley, 2024) Sánchez Sánchez, Juan Luis; Izquierdo Redín, Mikel; López Sáez de Asteasu, Mikel; Antón Rodrigo, Iván; Galbete Jiménez, Arkaitz; Álvarez Bustos, Alejandro; Casas Herrero, Álvaro; Ciencias de la Salud; Osasun Zientziak; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika; Institute of Smart Cities - ISC
In this response letter, we would like to clarify some aspects related to the methodology and inferences derived from our work entitled 'Effects of Vivifrail multicomponent intervention on functional capacity', which was aimed at investigating the effects of a home-based multicomponent individualized exercise programme (Vivifrail) on the functional capacity of frail older adults with mild cognitive impairment/dementia. Yan et al.2 raised concerns related to the amount of data missingness and methods used to handle it in our study. Although we addressed this issue as a limitation of the Discussion section of the original report, we now take the opportunity to further discuss its implications.
Open Access
Comparison of experiment and simulation of ultrasonic mid-air haptic forces
(IEEE, 2022) Morales González, Rafael; Georgiou, Orestis; Marzo Pérez, Asier; Frier, William; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika; Institute of Smart Cities - ISC
Ultrasound mid-air haptics is an emerging technology with many applications in human-computer interactions. Despite great advances in related hardware and software, physics models that predict the resulting forces on a surface (e.g., someone's hand) are either too simple (inaccurate) or too complex (computationally expensive). In this paper, we show that simple models are not sufficient when predicting the force on an experimental setup involving two prototype devices and a precision scale. Specifically, we demonstrate that our experimental measurements cannot be accurately predicted using a linear acoustic model.
Open Access
Constructing interval-valued fuzzy material implication functions derived from general interval-valued grouping functions
(IEEE, 2022) Pereira Dimuro, Graçaliz; Santos, Helida; Da Cruz Asmus, Tiago; Wieczynski, Jonata; Pinheiro, Jocivania; Bedregal, Benjamin; Bustince Sola, Humberto; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika; Institute of Smart Cities - ISC
Grouping functions and their dual counterpart, overlap functions, have drawn the attention of many authors, mainly because they constitute a richer class of operators compared to other types of aggregation functions. Grouping functions are a useful theoretical tool to be applied in various problems, like decision making based on fuzzy preference relations. In pairwise comparisons, for instance, those functions allow one to convey the measure of the amount of evidence in favor of either of two given alternatives. Recently, some generalizations of grouping functions were proposed, such as (i) the n-dimensional grouping functions and the more flexible general grouping functions, which allowed their application in n-dimensional problems, and (ii) n-dimensional and general interval-valued grouping functions, in order to handle uncertainty on the definition of the membership functions in real-life problems. Taking into account the importance of interval-valued fuzzy implication functions in several application problems under uncertainty, such as fuzzy inference mechanisms, this paper aims at introducing a new class of interval-valued fuzzy material implication functions. We study their properties, characterizations, construction methods and provide examples.