Secure Distributed System inspired by Ant Colonies for Road Traffic Management in Emergency Situations

We have proposed an algorithm, based on ant colonies, for road traffic management. The implementation of the algorithm does not rely on fixed infrastructures in order to operate in emergency situations. It only uses the VANET V2V communications and location systems that do not require contact with a fixed infrastructure. The algorithm uses signature aggregation and reputation lists to ensure system security. Furthermore, the algorithm has an implicit security that minimizes the risks in case of attacks. A scale prototype has been designed and implemented to validate the algorithm using RFID location system.In this work, we present a distributed system designed for road traffic management. The system is inspired by the behavior of the ant colonies. The distributed design responds to the particular limitations of an emergency situation; mainly, the fixed infrastructures are out of service because no energy supply is available. The implementation is based on the VANET facilities complemented with passive RFID tags or GPS localization. The vehicles can use the information of previous vehicles to dynamically decide the best path. A scale prototype has been developed to validate the system. It consists of several small size robotic vehicles, a test road circuit and a visual monitorization system. The security of the system is provided by a combination of data aggregation and reputation lists.Proyecto TIN 2011-25452 (TUERI: Technologies for secUre and Efficient wiReless networks within the Internet of things with applications in transport and logistic). Y Universidad de Málaga-Campus de Excelencia Internacional Andalucia Tech

PET image classification using HHT-based features through fractal sampling

Medical image classification is currently a challenging task that can be used to aid the diagnosis of different brain diseases. Thus, exploratory and discriminative analysis techniques aiming to obtain rep- resentative features from the images, play a decisive role in the design of effective Computer Aided Diagnosis (CAD) systems, which is spe- cially important in the early diagnosis of dementias. In this work we present a technique that allows extracting discriminative features from Positron Emission Tomography (PET) by means of an Empirical Mode Decomposition-based (EEMD) method. This requires to transform the 3D PET image into a time series which is addressed by sampling the image using a fractal-based method which allows to preserve the spa- tial relationship among voxels. The devised technique has been used to classify images from the Alzheimer's Disease Neuroimaging Initiat- ive (ADNI) achieving up to a 90.5% accuracy in a differential diagnosis task (AD vs. controls), which proves that the information retrieved by our methodology is significantly linked to the disease.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Avoiding relapses after crises: Exploring the influence of firm investors’ characteristics on organizational resilience

Many firms may successfully navigate an organizational crisis, but may find themselves entangled in another soon after. Building on a resource-dependence perspective, this study evaluates how certain investor characteristics foster organizational resilience during a crisis by preventing a relapse following recovery. Drawing on data from 2014 to 2019, we analyzed 359 firms that faced a crisis in 2015, as indicated by their Altman Z-score values. Our findings reveal that diversity and patience of investors prevent firms from relapsing into upcoming crises; however, the probability of relapse increases when concentrated investors boost the firm’s capital during the in-crisis period. We bridge the gap between the resource-dependence theory and literature on organizational resilience and contribute by extending previous analyses on the relevance of investors to recover from a crisis to identify how in-crisis investors’ features also state the foundations to avoid future relapses.Grant PID2019- 107767GA-I00 and Grant PID2022-138331NB-I00 funded by MICIU/AEI /10.13039/501100011033ERDF/UEGrant TED2021-129829B-I00 funded by MICIU/AEI/10.13039/5011 00011033European Union NextGenerationEU/PRTRGrant C-SEJ-069-UGR23 funded by Consejería de Universidad, Investigación e InnovaciónERDF Andalusia Progra