- Artículos de Revistas
ItemPhotophysical characterization of tetrahydroxyphenyl porphyrin Zn(II) and V(IV) complexes: experimental and DFT study(Springer Nature, 2023-06-21) Diaz-Uribe, Carlos; Rangel, Daily; Vallejo, William; Valle, Roger; Hidago-Rosa, Yoan; Zarate, Ximena; Schott, EduardoPhotodynamic therapy (PDT) is a promising technique for the treatment of various diseases. In this sense, the singlet oxygen quantum yield (Φ∆) is a physical–chemical property that allows to stablish the applicability of a potential photosensitizers (PS) as a drug for PDT. In the herein report, the Φ∆ of three photosensitizers was determined: metal-free tetrahydroxyphenyl porphyrin (THPP), THPP-Zn and the THPP-V metal complexes. Their biological application was also evaluated. Therefore, the in vitro study was carried out to assess their biological activity against Escherichia coli. The metal-porphyrin complexes exhibited highest activities against the bacterial strain Escherichia coli. at the highest concentration (175 μg/mL) and show better activity than the free base ligand (salts and blank solution). Results indicated a relation between Φ∆ and the inhibitory activity against Escherichia coli, thus, whereas higher is the Φ∆, higher is the inhibitory activity. The values of the Φ∆ and the inhibitory activity follows the tendency THPP-Zn > THPP > THPP-V. Furthermore, quantum chemical calculations allowed to gain deep insight into the electronic and optical properties of THPP-Zn macrocycle, which let to verify the most probable energy transfer pathway involved in the singlet oxygen generation. ItemML models for severity classification and length-of-stay forecasting in emergency units(Elsevier, 2023-03-02) Candia-Véjar, Alfredo; Moya-Carvajal, Jonathan; Pérez-Galarce, Francisco; Taramasco, Carla; Astudillo, César ALength-of-stay (LoS) prediction and severity classification for patients in emergency units in a clinic or hospital are crucial problems for public and private health networks. An accurate estimation of these parameters is essential for better planning resources, which are usually scarce. Although it is possible to find several works that propose traditional Machine Learning (ML) models to face these challenges, few works have exploited advances in Natural Language Processing (NLP) on Spanish raw-text vector representations. Consequently, we take advantage of those advances, incorporating sentence embeddings in traditional ML models to improve predictions. Moreover, we apply a strategy based on SHapley Additive exPlanations (SHAP) values to provide explanations for these predictions. The results of our case study demonstrate an increase in the accuracy of the predictions using raw text with a minimum preprocessing. The precision increased by up to 2% in the classification of the patient’s post-care destination and by up to 8% in the prediction of LoS in the hospital. This evidence encourages practitioners to use available text to anticipate the patient’s need for hospitalization more accurately at the earliest stage of the care process. ItemDesign of an EEG analytical methodology for the analysis and interpretation of cerebral connectivity signals(Elsevier, 2022-02-20) Córdova, Felisa M.; Cifuentes, Hugo F.; Díaz, Hernán A.; Yanine, Fernando; Pereira, RobertinoThe objective of this study is to design an Electroencephalographic (EEG) analytic methodology that allows to develop a variety of analysis and interpretations of brain signals. The initial phase considers the acquisition and filtering of EEG signals, the division into bands in data ranges, and the storage of EEG signals in a cloud data base. Then, an analytical phase considering descriptive, predictive and prescriptive analysis is accomplished. A sequence of analytic intermediate processing steps is done in order to render a graphic visualization of significant correlations between pairs of EEG channels. Pearson correlation is utilized to detect synchronic connectivity through the brain areas. Time series in nearly instantaneous time lapses are treated by using Hilbert Huang Transform. An experimental design by submitting a set of students to an abbreviated version Raven visual test is made providing results in correlation maps of cerebral connectivity ItemOn a Pickup to Delivery Drone Routing Problem: Models and algorithms(Elsevier, 2022-09-06) Gómez-Lagos, Javier; Rojas-Espinoza, Benjamín; Candia-Véjar, AlfredoA new variant of the Pickup and Delivery Routing problem is presented. Given a set of customers, facilities, a depot, and a homogeneous fleet of drones, the Pickup to Delivery Drone Routing Problem (PDDRP) aims to find a drone scheduling such that a drone serves the customer’s order from a set of available facilities. Each drone starts in the depot, flies to pickup the customer’s order in a facility, and continues its flight to deliver the parcel to a customer. Then, the drone begins another service, and once its last service is completed, it returns to the depot. The objective is to minimize the makespan associated with the drone fleet. The layer of facilities forcing drones to visit one of them to pickup the parcel makes the problem different from traditional pickup and delivery routing problems. Three mixed-linear programming models are presented to obtain optimal solutions for the problem. The first model is related to the multiple Traveling Salesman Problem (m-TSP), the second is associated with the Parallel Machine Scheduling Problem (PMS), and the third was developed specifically for the new problem. Given the high computational complexity of the PDDRP, a Greedy Randomized Adaptive Search Procedure (GRASP) was designed to find near-optimal solutions when exact approaches cannot achieve (near) optimal solutions. Computational experiments show that a commercial solver could solve only small problem instances. GRASP can find reasonable solutions in a short time when medium and large instance sizes need to be solved. Finally, is shown that some routing problems for delivery, allowing truck-drone collaboration, could be formulated as an extension of PMS.