University of La Laguna Showcase

There are currently numerous interactions between anthropogenic activities and wildlife. These activities have caused a decline in sea turtle populations in recent decades. Spanish waters, both on the mainland and on the islands, are staging grounds for these animals, which spend part of their life cycle there, mainly for Caretta caretta. However, it is in these same waters where they are threatened due to various causes of stranding. This study attempts to gain an in-depth understanding of the different phases of the rehabilitation process of these animals at the WRC "La Tahonilla" in Tenerife, Canary Islands. It also aims to learn about the development of this process in the different rehabilitation centres aimed at recovering sea turtles on the mainland and the Canary Islands. We have also studied in depth the process of dealing with a stranded sea turtle in the Canary Islands, which do not have their own recovery centre. The results of this study have revealed the existence of different protocols for action between centres and islands. The improvement and unification of these protocols is of vital importance to improve the rehabilitation of sea turtles in Spain.

Bases de datos modelo explicativo riesgo de suicidio

The dataset provides a summary of the experimental results obtained from an HCAI system implemented using a RAG framework and the Llama 3.0 model. A total of 125 hyperparameter configurations were defined by aggregating metrics based on the median of the results from 91 questions and their corresponding answers. These configurations represent the alternatives evaluated through Multi-Criteria Decision-Making (MCDM) methods.

Genpop file obtained using 2b-RADseq which comprises a total of 14,881 loci for 74 individuals of the black sea urchin Arbacia lixula. The individuals were collected along a natural CO2 gradient from the island of La Palma in the Canary Archipelago, Spain.

The data reflect the results of the experimentation with an HCAI system implemented using a RAG framework and the Llama 3.0 model. During the experimentation, 91 questions were utilized in the domain of legal advice and migrant rights. Metrics assessed included contextual enrichment, textual quality, discourse analysis, and sentiment evaluation. This allows for the analysis of sentiments and emotions, bias detection, content and toxicity classification, as well as an analysis of inclusion and diversity.

Theme_Identifiability_Ad_hoc_Spanish.xls consists of five sheets. The first sheet, "Theme identifiability", contains the raw data. For each participant, it presents the 70 critical words along with their respective lists of 10 ad hoc associates. The first column shows the participant number, the second column identifies the list, the third column specifies the type of relationship of the lists (ad hoc), and the fourth column indicates the type of word (studied vs. critical). The fifth column contains the words themselves, while the sixth column provides the English translations of the critical and studied words. Adjacent columns include all responses provided by participants in the first, second, and third positions, as well as the confidence judgements related to the first response. Additionally, intrusions are noted—i.e., words from a study list that a participant mistakenly identified as the theme of that list, and therefore are not considered valid responses. The second sheet, “First word”, contains the total count of responses given in the first position for each list. It includes all the words that participants generated as the theme in the first position, associated with their respective critical word and list number. Additionally, it provides the English translation of the critical word, the total number of participants who responded to each list, the number of participants who identified a word as the theme, the percentage of participants who did so, and the mean confidence judgement for each first word. Intrusions are also noted, including the quantity and the incorrectly identified word. The third and fourth sheets, “Second word” and “Third word”, respectively, are dedicated to the count of responses given in the second and third positions. The layout is identical to the previous sheets, except that the column for the mean confidence judgement is not included. The fifth sheet, "Summary," presents the final summary, showing the most frequently identified theme for the first, second, and third positions for each list.

Dataset of Computer Aided Drug Design (CADD) and Molecular Dynamics (MD) operations for the article, titled "NEW DYNAMIC SCORING METHOD FOR DEEP EVALUATION OF NALOXEGOL AS Β-TUBULIN BINDING INHIBITOR"

Raw results from cell and animal experiments

There are new benchmark instances for the Cross-Dock Door Assignment and Scheduling Problem (CDSP), with instances from eight origins and destinations, four inbound and outbound doors, up to 50 origins and destinations, and 30 inbound and outbound doors. The instances have been obtained following the criteria of the paper by Nassief et al. (2016), with some modifications. First of all, the number of pallets to be moved from a supplier to a customer is randomly generated by using a uniform distribution U[1,5] until density values are set to 25, 35, 50, and 75%. Each inbound truck sends pallets from at least one outgoing truck, and each outgoing truck receives pallets from at least one incoming truck. The distance matrix is generated with numbers from the interval [1, 1 + |I|- 1], meaning that a direct distance between two doors is equal to 1, and then an increment of 1 unit is added for the next indirect door. The number of considered incoming/outgoing trucks is 8, 9, 10, 11, 12, 15, 20, and 50. The number of considered inbound/outbound doors is 4, 5, 6, 7, 10, and 30. The door capacities are equal for each instance and calculated by dividing the total flow coming from all origins by the total number of inbound doors and then adding a capacity slackness of 5, 10, 15, 20, and 30%. To generate this set of instances, we have considered the combinations of values reported in the paper by Sayed (2020). The instances are referred to as 00×00×00x00 (number of the incoming and outgoing trucks x number of inbound and outbound doors x capacity slackness associated with the inbound/outbound doors - 5, 10, 15, 20, and 30% - x density of the flow matrix - 25, 35, 50, and 75%).

A collection of images created by Midjourney using various promtps to understand the AI's biases in character concept art.