Individuals develop innovative behaviours to solve foraging challenges in the face of changing environmental conditions. Little is known about how individuals differ in their tendency to solve problems and in their subsequent use of this solving behaviour in social contexts. Here we investigated whether individual variation in problem-solving performance could be explained by differences in the likelihood of solving the task, or if they reflect differences in foraging strategy. We tested this by studying the use of a novel foraging skill in groups of great tits (Parus major), consisting of three naive individuals with different personality, and one knowledgeable tutor. We presented them with multiple, identical foraging devices over eight trials. Though birds of different personality type did not differ in solving latency; fast and slow explorers showed a steeper increase over time in their solving rate, compared to intermediate explorers. Despite equal solving potential, personality influenced the subsequent use of the skill, as well as the pay-off received from solving. Thus, variation in the tendency to solve the task reflected differences in foraging strategy among individuals linked to their personality. These results emphasize the importance of considering the social context to fully understand the implications of learning novel skills.
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Sci Rep
January 2025
School of Electromechanical Engineering, Guangdong University of Technology, Guangzhou, 510006, China.
Multi-objective and multi-stage decision-making problems require balancing multiple objectives at each stage and making optimal decision in multi-dimensional control variables, where the commonly used intelligent optimization algorithms suffer from low solving efficiency. To this end, this paper proposes an efficient algorithm named non-dominated sorting dynamic programming (NSDP), which incorporates non-dominated sorting into the traditional dynamic programming method. To improve the solving efficiency and solution diversity, two fast non-dominated sorting methods and a dynamic-crowding-distance based elitism strategy are integrated into the NSDP algorithm.
View Article and Find Full Text PDFNurse Educ Pract
January 2025
Grupo de Innovación Docente INTERMASTER, Universitat de Barcelona, Spain; Grupo de Innovación Docente IDhEA-Fundación Index, Spain; Departament d'Infermeria Fonamental i Clínica, Facultat d´Infermeria, Universitat de Barcelona, Spain. Electronic address:
Aim: To explore the elements of nursing identity recognized by nursing students in models developed through the 'Design process' methodology.
Background: The construction of nursing professional identity is a complex process involving identification, group belonging, partial assessments and social representations. Nursing identity is one of the most stereotyped and its formation during the nursing degree has a significant impact on professional development.
Int J Lang Commun Disord
January 2025
Department of Language and Cognition, University College London, London, UK.
Background: Global aphasia is a severe communication disorder affecting all language modalities, commonly caused by stroke. Evidence as to whether the functional communication of people with global aphasia (PwGA) can improve after speech and language therapy (SLT) is limited and conflicting. This is partly because cognition, which is relevant to participation in therapy and implicated in successful functional communication, can be severely impaired in global aphasia.
View Article and Find Full Text PDFSci Diabetes Self Manag Care
January 2025
School of Nursing, Capital Medical University, Beijing, China.
Purpose: The purpose of the study was to explore the facilitators and barriers of health behaviors in patients with type 2 diabetes (T2D), providing a reference for the development of health behavior interventions programs.
Methods: A qualitative descriptive research design was adopted, and interviews were conducted with 25 patients with T2D. The interview guide was developed based on the health action process approach theory.
Neural Netw
January 2025
School of Engineering, Brown University, United States of America; Division of Applied Mathematics, Brown University, United States of America. Electronic address:
Multi-task learning (MTL) is an inductive transfer mechanism designed to leverage useful information from multiple tasks to improve generalization performance compared to single-task learning. It has been extensively explored in traditional machine learning to address issues such as data sparsity and overfitting in neural networks. In this work, we apply MTL to problems in science and engineering governed by partial differential equations (PDEs).
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