Jose Manuel

Hospital QuironSalud Córdoba, 14004 Córdoba, Spain Instituto Maimónides de Investigación Biomédica de Córdoba (IMIBIC), Universidad de Córdoba, 14004 Córdoba, Spain

Cross-sectional energy benchmarking in the building domain has become crucial for policymakers, energy managers and property owners as they can compare an immovable property performance against its closest peers. For this, Key Performance Indicator (KPIs) are formulated, often relying on multiple and heterogeneous data sources which, combined, can be used to set benchmarks following normalization criteria. Geographically delimited parameters are important among these criteria because they enclose entities sharing key common characteristics the geometrical boundaries represent. Linking georeferenced heterogeneous data is not trivial, for it requires geographical aggregation, which is often taken for granted or hidden within a pre-processing activity in most energy benchmarking studies. In this article, a novel approach for Linked Data (LD) generation is presented as a methodological solution for data integration together with its application in the energy benchmarking use case. The methodology consists of eight phases that follow the best principles and recommend standards including the well-known GeoSPARQL Open Geospatial Consortium (OGC) for leveraging the geographical aggregation. Its feasibility is demonstrated by the integrated exploitation of INSPIRE-formatted cadastral data and the Buildings Performance Certification (BPCs) available for the Catalonia region in Spain. The outcomes of this research support the adoption of the proposed methodology and provide the means for generating cross-sectional building energy benchmarking histograms from any-scale geographical aggregations on the fly.

In recent years, the implementation of shared electric micro-mobility services (SEMMS) enables short rentals of light electric vehicles for short-distance travel. The fast expansion of SEMMS worldwide, promoted as a green mobility service, has raised a debate about its role in urban mobility, especially in terms of environmental impacts such as climate change. This article presents a systematic review of the current knowledge on the environmental impacts of SEMMS, with a special focus on the use of life-cycle assessment (LCA) methods. The study offers a detailed analysis of the global warming potential of SEMMS and its critical phases. It is found that shared e-scooters have the greatest greenhouse-gas emissions during their life cycle, while emissions from shared e-mopeds and shared e-bikes tend to be lower. The literature reveals that the materials and manufacturing phase is the most important one for the environmental impact of shared e-scooters, followed by the daily collection of vehicles for charging. The article also identifies influential factors in the sensitivity analysis and the potential for net-impact reduction accounted for mode substitution. Finally, the article identifies further research areas aimed at contributing to the adoption of environmentally responsible practices in the rapidly expanding field of shared services in cities.

Biobanks are infrastructures essential for research involving multi-disciplinary teams and an increasing number of stakeholders. In the field of personalized medicine, biobanks play a key role through the provision of well-characterized and annotated samples protecting at the same time the right of donors. The Andalusian Public Health System Biobank (SSPA Biobank) has implemented a global information management system made up of different modules that allow for the recording, traceability and monitoring of all the information associated with the biobank operations. The data model, designed in a standardized and normalized way according to international initiatives on data harmonization, integrates the information necessary to guarantee the quality of results from research, benefiting researchers, clinicians and donors.

Abstract Addressing subcapital fractures of the femur poses a substantial clinical challenge, complicated by the diverse range of available osteosynthesis materials.

Laboratorio de Química Teórica, Centro de Investigación, Departamento de Fisicoquímica, Facultad de Ciencias Químicas, Benemérita Universidad Autónoma de Puebla, Edif.

Severe Infection Research Group, Health Research Institute La Fe, 46026 Valencia, Spain

Computational modeling (CM) is a versatile scientific methodology used to examine the properties and behavior of complex systems, such as polymeric materials for biomedical bioengineering. CM has emerged as a primary tool for predicting, setting up, and interpreting experimental results. Integrating in silico and in vitro experiments accelerates scientific advancements, yielding quicker results at a reduced cost. While CM is a mature discipline, its use in biomedical engineering for biopolymer materials has only recently gained prominence. In biopolymer biomedical engineering, CM focuses on three key research areas: (A) Computer-aided design (CAD/CAM) utilizes specialized software to design and model biopolymers for various biomedical applications. This technology allows researchers to create precise three-dimensional models of biopolymers, taking into account their chemical, structural, and functional properties. These models can be used to enhance the structure of biopolymers and improve their effectiveness in specific medical applications. (B) Finite element analysis, a computational technique used to analyze and solve problems in engineering and physics. This approach divides the physical domain into small finite elements with simple geometric shapes. This computational technique enables the study and understanding of the mechanical and structural behavior of biopolymers in biomedical environments. (C) Molecular dynamics (MD) simulations involve using advanced computational techniques to study the behavior of biopolymers at the molecular and atomic levels. These simulations are fundamental for better understanding biological processes at the molecular level. Studying the wide-ranging uses of MD simulations in biopolymers involves examining the structural, functional, and evolutionary aspects of biomolecular systems over time. MD simulations solve Newton's equations of motion for all-atom systems, producing spatial trajectories for each atom. This provides valuable insights into properties such as water absorption on biopolymer surfaces and interactions with solid surfaces, which are crucial for assessing biomaterials. This review provides a comprehensive overview of the various applications of MD simulations in biopolymers. Additionally, it highlights the flexibility, robustness, and synergistic relationship between in silico and experimental techniques.

Colon cancer is a prevalent and potentially fatal disease that demands early and accurate diagnosis for effective treatment. Traditional diagnostic approaches for colon cancer often face limitations in accuracy and efficiency, leading to challenges in early detection and treatment. In response to these challenges, this paper introduces an innovative method that leverages artificial intelligence, specifically convolutional neural network (CNN) and Fishier Mantis Optimizer, for the automated detection of colon cancer. The utilization of deep learning techniques, specifically CNN, enables the extraction of intricate features from medical imaging data, providing a robust and efficient diagnostic model. Additionally, the Fishier Mantis Optimizer, a bio-inspired optimization algorithm inspired by the hunting behavior of the mantis shrimp, is employed to fine-tune the parameters of the CNN, enhancing its convergence speed and performance. This hybrid approach aims to address the limitations of traditional diagnostic methods by leveraging the strengths of both deep learning and nature-inspired optimization to enhance the accuracy and effectiveness of colon cancer diagnosis. The proposed method was evaluated on a comprehensive dataset comprising colon cancer images, and the results demonstrate its superiority over traditional diagnostic approaches. The CNN–Fishier Mantis Optimizer model exhibited high sensitivity, specificity, and overall accuracy in distinguishing between cancer and non-cancer colon tissues. The integration of bio-inspired optimization algorithms with deep learning techniques not only contributes to the advancement of computer-aided diagnostic tools for colon cancer but also holds promise for enhancing the early detection and diagnosis of this disease, thereby facilitating timely intervention and improved patient prognosis. Various CNN designs, such as GoogLeNet and ResNet-50, were employed to capture features associated with colon diseases. However, inaccuracies were introduced in both feature extraction and data classification due to the abundance of features. To address this issue, feature reduction techniques were implemented using Fishier Mantis Optimizer algorithms, outperforming alternative methods such as Genetic Algorithms and simulated annealing. Encouraging results were obtained in the evaluation of diverse metrics, including sensitivity, specificity, accuracy, and F1-Score, which were found to be 94.87%, 96.19%, 97.65%, and 96.76%, respectively.

Open Access Review by Daniel Caballero-Martin Daniel Caballero-Martin SciProfiles Scilit Preprints.org