Nallig Leal

This study analyzes the role of blockchain technology in the digital transformation of businesses, focusing on its adoption, sectoral applications, challenges, investment trends, and integration with emerging technologies such as artificial intelligence (AI). The research employs a technology surveillance methodology in its analytical phase, supported by a documentary review of 69 sources published between 2017 and 2025. The findings reveal that blockchain has evolved beyond its original use in cryptocurrencies and is increasingly applied in finance, healthcare, logistics, education, and public administration. Key benefits identified include enhanced traceability, operational efficiency, and transparency. However, several barriers persist, such as technological infrastructure limitations, high implementation costs, regulatory uncertainty, and a shortage of specialized talent. The study also highlights a growing convergence of blockchain and AI, as well as accelerated adoption among small and medium-sized enterprises (SMEs) and in emerging markets. The conclusions position blockchain as a strategic enabler of business transformation, with strong potential to reshape business models and increase competitiveness. As a practical implication, the study recommends implementing adoption strategies that address investment, talent development, and legal frameworks to fully leverage blockchain’s long-term impact in digital markets.

La tecnología blockchain ha evolucionado más allá de sus orígenes en las criptomonedas, y actualmente sustenta aplicaciones y plataformas que interactúan con redes descentralizadas. Esto es particularmente evidente en mercados de NFT, gestión de activos digitales y aplicaciones descentralizadas (DApps), donde blockchain garantiza transacciones seguras y transparentes. A medida que su adopción se amplía, la incorporación de datos generados por blockchain en sistemas de software plantea desafíos importantes en diseño de sistemas, integración de bases de datos y prácticas de desarrollo. Este artículo ofrece una revisión integral del impacto de blockchain en los requisitos de software, arquitecturas de bases de datos, estrategias de desarrollo y principios de diseño de sistemas, áreas frecuentemente subestimadas en la investigación convencional sobre blockchain. Al analizar tendencias actuales, desafíos clave y buenas prácticas, el estudio proporciona conocimientos prácticos sobre cómo blockchain se vincula con la ingeniería de software y contribuye al desarrollo de soluciones digitales modernas.

With the advancements in neuroimaging techniques, understanding the relationship between brain morphology and behavioral tendencies such as criminal behavior has garnered interest. This research addresses the investigation of disparities in neuroanatomical structures between adolescent offenders and non-offenders and considers the implications of such distinctions regarding offender behavior within adolescent populations. Employing data-driven methodologies, MRI scans of adolescents from Barranquilla, Colombia, were analyzed to explore morphological variations. Utilizing a 1.5 Tesla Siemens resonator (Siemens Healthineers, Erlangen, Germany), T1-weighted MPRAGE anatomical images were acquired and analyzed using a systematic five-step methodology including data acquisition, MRI pre-processing, feature selection, model selection, and model validation and evaluation. Participants, both offenders and non-offenders, were aged 14–18 and selected based on education, criminal history, and physical conditions. The research identified significant disparities in the volumes of 42 brain structures between adolescent offenders (AOs) and non-offenders (NOs), highlighting particular brain regions potentially associated with offending behavior. Additionally, a considerable proportion of AOs emanated from lower socioeconomic backgrounds and showcased marked substance use. The findings suggest that neuroanatomical disparities potentially correlate with criminal behavior among adolescents at a neurobiological level. Noticeable socio-environmental factors, such as lower socioeconomic status and substance abuse, were substantially prevalent among AOs. Particularly, neurobiological deviations in structures like ctx-lh-rostralmiddlefrontal and ctx-lh-caudalanteriorcingulate perhaps represent a link between neurological factors and external stimuli.

High-resolution 3D scanning devices produce high-density point clouds, which require a large capacity of storage and time-consuming processing algorithms. In order to reduce both needs, it is common to apply surface simplification algorithms as a preprocessing stage. The goal of point cloud simplification algorithms is to reduce the volume of data while preserving the most relevant features of the original point cloud. In this paper, we present a new point cloud feature-preserving simplification algorithm. We use a global approach to detect saliencies on a given point cloud. Our method estimates a feature vector for each point in the cloud. The components of the feature vector are the normal vector coordinates, the point coordinates, and the surface curvature at each point. Feature vectors are used as basis signals to carry out a dictionary learning process, producing a trained dictionary. We perform the corresponding sparse coding process to produce a sparse matrix. To detect the saliencies, the proposed method uses two measures, the first of which takes into account the quantity of nonzero elements in each column vector of the sparse matrix and the second the reconstruction error of each signal. These measures are then combined to produce the final saliency value for each point in the cloud. Next, we proceed with the simplification of the point cloud, guided by the detected saliency and using the saliency values of each point as a dynamic clusterization radius. We validate the proposed method by comparing it with a set of state-of-the-art methods, demonstrating the effectiveness of the simplification method.

Magnetic Resonance (MR) Imaging is a diagnostic technique that produces noisy images, which must be filtered before processing to prevent diagnostic errors. However, filtering the noise while keeping fine details is a difficult task. This paper presents a method, based on sparse representations and singular value decomposition (SVD), for non-locally denoising MR images. The proposed method prevents blurring, artifacts, and residual noise. Our method is composed of three stages. The first stage divides the image into sub-volumes, to obtain its sparse representation, by using the KSVD algorithm. Then, the global influence of the dictionary atoms is computed to upgrade the dictionary and obtain a better reconstruction of the sub-volumes. In the second stage, based on the sparse representation, the noise-free sub-volume is estimated using a non-local approach and SVD. The noise-free voxel is reconstructed by aggregating the overlapped voxels according to the rarity of the sub-volumes it belongs, which is computed from the global influence of the atoms. The third stage repeats the process using a different sub-volume size for producing a new filtered image, which is averaged with the previously filtered images. The results provided show that our method outperforms several state-of-the-art methods in both simulated and real data.

In brain magnetic resonance images (brain MRI) analysis, for diagnosing certain brain conditions, it is necessary to quantify the brain tissue, which implies to separate the brain from extracranial or non-brain tissues through a process of isolation known as skull stripping. This is a non-trivial task since different types of tissues may have the same gray level, and during the separation process, some brain tissues could be removed. This paper presents a new solution approach for the skull stripping problem, based on saliency detection using dictionary learning and sparse coding, which can operate over T1 and T2 weighted axial brain MRI. Our method first subdivides the axial MRI into full overlapped patches and runs a dictionary learning over them for obtaining its sparse representation. Then, by analyzing the sparse coding matrix, we compute how many patches a dictionary atom affects to classify them as frequent or rare. Then, we calculate the saliency map of the axial MRI according to the composition of the image patches, i.e. an image patch is considered salient if it is mainly composed of frequent atoms, an atom is frequent whether it affects many patches. The non-salient pixels, corresponding to non-brain tissues, are eliminated from the MRI. Numerical results validate our method

Introduction: The use of mobile applications has increased in the last years. Most of them require the knowledge of the user location, either for their core service or for marketing purposes. Location-based services (LBS) offer context-based assistance to users based on their location. Although these applications ask the user for permission to use their location and even explain in detail how this information will be used in its terms and conditions, most users are not aware or even interested in the fact that their location information is stored in databases and monetized by selling it to third-party companies. Regarding this situation, we developed a study with the aim to assess perception, concerns and awareness from users about their location information. Methods: This work is based on an exploratory survey applied to the university community, mainly from the North Coast of Colombia, to measure the perception of location privacy of users with mobile devices. The questionnaire was applied using Google Forms. The survey has nineteen questions organized in three sections: personal information, identification of privacy and privacy management. These questions were designed to know the users’ perceptions of privacy concerns in LBS and any actions they take to preserve it. Results: The results show that, in general, the respondents do not have a real concern regarding the privacy of their geolocation data, and the majority is not willing to pay to protect their privacy. Conclusions: This type of surveys can generate awareness among participants about the use of their private information. The results expose in this paper can be used to create government policies and regulations by technology companies about the privacy management.