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Although many species display behavioural traditions, human culture is unique in the complexity of its technological, symbolic and social contents. Is this extraordinary complexity a product of cognitive evolution, cultural evolution or some interaction of the two? Answering this question will require a much better understanding of patterns of increasing cultural diversity, complexity and rates of change in human evolution. Palaeolithic stone tools provide a relatively abundant and continuous record of such change, but a systematic method for describing the complexity and diversity of these early technologies has yet to be developed. Here, an initial attempt at such a system is presented. Results suggest that rates of Palaeolithic culture change may have been underestimated and that there is a direct relationship between increasing technological complexity and diversity. Cognitive evolution and the greater latitude for cultural variation afforded by increasingly complex technologies may play complementary roles in explaining this pattern.

Collaboration in Multi-Agent Systems (MASs) is crucial but challenging in robotics, especially in heterogeneous MASs where robots have different capabilities. Nowadays, the key issue in research on collaboration in MASs is to fully utilize the capabilities of heterogeneous agents. To address this issue, we propose Auction-Based Behavior Tree Evolution (ABTE), a novel two-layer framework designed to learn BTs for heterogeneous MASs. In the first layer, we call it the command layer, and robots receive their tasks through the auction algorithm, enhanced by our innovative three-way handshaking communication protocol embedded in BT implementation, ensuring more efficient task allocation. The second layer of ABTE defines the specific execution behaviors of agents and is, therefore, named the execution layer. The behaviors in this layer are automatically generated by Grammatical Evolution (GE), which has been proven to be a general and effective method for generating swarm BTs. Our experiments are conducted within a Disaster Rescue Scenario, which requires intricate collaboration among multiple robots with diverse capabilities. The results indicate that ABTE outperforms the baseline algorithm, GEESE, in terms of resource utilization. Moreover, it demonstrates robust effectiveness in covering high-priority tasks, thereby validating the efficacy of employing an auction algorithm for generating BTs tailored for heterogeneous MAS.

Estimates of parameters that affect population dynamics, including the size at which individuals reproduce, are crucial for efforts aimed at understanding how imperiled species may recover from the numerous threats they face. In this study, we observed loggerhead marine turtles (Caretta caretta) nesting at three sites in the Gulf of Mexico at sizes assumed nonreproductive in this region (≤87 cm curved carapace length‐notch [CCL‐n]). These smaller individuals ranged from 74.0 to 86.9 cm CCL‐n, and the proportion of smaller nesting loggerheads was 0.13 across three study sites: Gulf Shores, AL; Dry Tortugas National Park, Florida (FL); and Everglades National Park (ENP), FL. The greatest proportion of smaller nesters was observed at ENP at 0.24. Tracking data indicated that the smaller nesters migrated shorter distances and swam in shallower waters compared to the larger nesting loggerheads (>87 cm CCL‐n) in our dataset. These results provide valuable information on two of the smallest subpopulations of NW Atlantic loggerheads and understudied ENP turtles. Our results have potential applications in the classification and interpretation of stranding limits and bycatch estimates, population modeling (e.g., stage durations and fecundity), and understanding threats and subpopulation recovery efforts for multiple subpopulations of this imperiled species. We observed nesting of imperiled loggerhead marine turtles at three sites in the Gulf of Mexico that were below the standard size considered reproductive for this species in this region (87 cm curved carapace length‐notch). Across the three sites, 13% of the loggerheads nesting were smaller than the 87 cm CCL‐n threshold. We also observed that these smaller nesters behaved differently and showed shorter migration distances and swam at shallower depths compared to their larger‐sized counterparts. Our results are applicable to the classification of stranding limits and bycatch estimates, population modeling, and evaluating recovery for this imperiled species.

The study of learning behaviors with multi features is of great significance for interactive cooperation. The data prediction and decision are to realize the comprehensive analysis and value mining. In this study, hierarchical learning behavior based on feature cluster is proposed. Based on the massive data in interactive learning environment, the descriptive model and learning algorithm suitable for feature clustering are designed, and sufficient experiments obtain the optimal performance indexes. The data analysis results are reliable. On this basis, the hierarchical learning behaviors based on feature clusters are visualized, the rules of different learning behaviors are summarized, then we propose the practical scheme of interactive cooperation. The hierarchical learning behaviors can be realized by feature clusters, which can effectively improve the modes of interactive cooperation, and help to improve the learning effectiveness.

In the globalized and digitized modern society, the cultivation of English listening and speaking ability has attracted more and more attention. With the development of information technology, the application of classroom online interactive platforms in English listening and speaking teaching is becoming increasingly widespread. However, existing studies focus on the technical performance and functionality of online interactive platforms, lacking a deep understanding of students’ actual learning behaviors and teachers’ using behaviors. Moreover, most of the studies analyze the platform effect at the macro level and neglect the impact of the student-teacher interactive mode at the micro level. Therefore, this research focused on studying the hierarchical clustering of students’ online learning behaviors and the typical student-teacher interactive mode of “resource-platform-need,” aiming to fill this gap. A data-driven research method was used, which was expected to provide new understanding and perspectives, bringing new theoretical and practical values to the educational technology field. The research results helped design and optimize the online interactive platform and better met the needs of students and teachers, thus promoting the development of English listening and speaking teaching.

This article discusses issues and perspectives related to the study of disruptive clinician behavior (DCB) to improve patient safety and healthcare professionals' work environments. Multiple terminologies and ambiguous definitions have resulted in conceptual confusion in studies on DCB. In addition, subjective classifications have led the attributes of DCB to overlap and fluctuate. Therefore, we share Rosenberg's definition of DCB as \"any inappropriate behavior, confrontation, or conflict, ranging from verbal abuse to physical and sexual harassment.\" It is recommended that DCB be understood as a hierarchical structure identified through statistical analysis of field survey data. Furthermore, a recurring list of items is duplicated across existing studies on DCB triggers, contributing factors, and influences. These items can be organized into separate path models based on their mutual relationships. Given these assumed models, we believe that further studies on DCB can shift toward elucidating the mechanisms of occurrence and impact. Finally, based on the path models, we recommend improving healthcare professionals' psychological and social states through a policy shift from \"zero-tolerance\" to \"to err is human\" as a priority issue for DCB prevention and countermeasures.

The multilevel approach can be a fruitful methodological framework in which to formulate the micro-macro relationships existing between individuals and their contexts. Usually, place of residence is taken as proxy for context. But individuals can be classified at the same level in more than one way. For example, not only may place of residence be relevant, but birthplace, household or working relations may also be taken into account. Contextual effects can be better identified if multiple classifications are simultaneously considered. in this sense, data do not have a purely hierarchical structure but a cross-classified one, and become very important to establish whether the resulting structure affects the covariance structure of data. In this paper, some critical issues arising from application of multilevel modelling are discussed, and multilevel cross-classified models are proposed as more flexible tools to study contextual effects. A multilevel cross-classified model is specified to evaluate simultaneously the effects of women's place of birth and women's current place of residence on the choice of bearing a second child by Italian women in the mid-1990s.[PUBLICATION ABSTRACT]

Background This study aim to explore the intentions, motivators and barriers of the general public to vaccinate against COVID-19, using both the Health Belief Model (HBM) and the Theory of Planned Behavior (TPB) model. Methods An online survey was conducted among Israeli adults aged 18 years and older from May 24 to June 24, 2020. The survey included socio-demographic and health-related questions, questions related to HBM and TPB dimensions, and intention to receive a COVID-19 vaccine. Associations between questionnaire variables and COVID-19 vaccination intention were assessed by univariate and multivariate analyses. Results Eighty percent of 398 eligible respondents stated their willingness to receive COVID-19 vaccine. A unified model including HBM and TPB predictor variables as well as demographic and health-related factors, proved to be a powerful predictor of intention to receive COVID-19 vaccine, explaining 78% of the variance (adjusted R squared = 0.78). Men (OR = 4.35, 95% CI 1.58–11.93), educated respondents (OR = 3.54, 95% CI 1.44–8.67) and respondents who had received the seasonal influenza vaccine in the previous year (OR = 3.31, 95% CI 1.22–9.00) stated higher intention to receive COVID-19 vaccine. Participants were more likely to be willing to get vaccinated if they reported higher levels of perceived benefits of COVID-19 vaccine (OR = 4.49, 95% CI 2.79–7.22), of perceived severity of COVID-19 infection (OR = 2.36, 95% CI 1.58–3.51) and of cues to action (OR = 1.99, 95% CI 1.38–2.87), according to HBM, and if they reported higher levels of subjective norms (OR = 3.04, 95% CI 2.15–4.30) and self-efficacy (OR = 2.05, 95% CI 1.54–2.72) according to TPB. Although half of the respondents reported they had not received influenza vaccine last year, 40% of them intended to receive influenza vaccine in the coming winter and 66% of them intended to receive COVID-19 vaccine. Conclusions Providing data on the public perspective and predicting intention for COVID-19 vaccination using HBM and TPB is important for health policy makers and healthcare providers and can help better guide compliance as the COVID-19 vaccine becomes available to the public.