Route Planning Process by the Endangered Black Lion Tamarin in Different Environmental Contexts

Daily, primates take a variety of decisions to establish why, when, and where to move. However, little is known about the factors influencing and shaping primate daily routes. We investigated the decision-making processes linked to route planning in four groups of black lion tamarins (BLT—Leontopithecus chrysopygus). We studied these endangered platyrrhines within four distinct environmental contexts across their natural distribution (i.e., a continuous forest, a 500-ha forest fragment, a 100-ha forest fragment, and a riparian forest). We used the Change Point Test to identify the points of significant direction change (CPs), which can be considered travel goals along BLT daily trajectories and are key components of travel planning. Considering the high importance of fruits and gum in BLT's diet, we predicted that feeding trees would be the main factor shaping their paths (feeding CPs-FCPs). Also, given previous evidence that platyrrhines use landmarks (i.e., characteristic features from the terrain) as nodes in route network systems (i.e., points of intersection connecting habitual route segments), we expected part of CPs to be located close to the intersection points and to be associated with “locomotion” behavior (LCPs). Analyzing 61 daily paths in four forest fragments, our results showed that BLTs planned routes to reach feeding trees, which primarily determined path orientation. As hypothesized, locomotion was the most frequent behavior observed in CPs, but only in the continuous and riparian forests, with LCPs located as close to intersections as FCPs. Interestingly, these two areas presented the most extreme values (i.e., higher and lower values, respectively) in terms of used area, richness of resources and distances traveled between fruit-feeding trees. Our results suggest that BLTs plan daily routes conditional on the environmental context to reach travel goals, likely to maximize route efficiency to reach out of sight feeding trees

Methodological reflections on tracing networked images

Purpose: Many scholars highlight a need for reflexive methodological accounts to support visual research. Therefore, this paper offers detailed reflection on the methods involved in tracing and analysing 248 commercial images of entrepreneurship. This account supports our published work examining entrepreneurial masculinities and femininities, which conceptualised the gendering of entrepreneurial aesthetics, and proposed the significance of image networks in the reproduction of neoliberal ideals. Design/Methodology/Approach:Now based on further methodological reflexivity we offer insights on both the possibilities and challenges of tracing networked images by reviewing four methodological complexities: reflexive engagement with online images; working with and across platforms; tracing as a potentially never-ending process; and montage approaches to analysis. Findings: Our account focuses on a specific form of imagery – commercial images – on a certain representation – the gendered entrepreneur – and on a particular complex site of encounter – online. This work mapped a visual repertoire of gendered entrepreneurship online by tracing visual constructions of entrepreneurial masculinity and femininity. In this paper we open the methodological ‘black box’ of our study and explain our belief that methodological advances can only be built through exposing our working practice. Originality: Through our detailed reflective account we aim to open discussions to aid development and use of complex visual methods online

Oxygen isotope dendrochronology allows dating of historical timbers across a wide geographical region

We explore the applicability and geographic reach of two northwest European stable oxygen isotope (δ18O) chronologies for the precision dating of annually resolved δ18O series developed from late 15th-century oak (Quercus sp.) roof timbers from St. James’ Church in Bruges, Belgium. In doing so this study assesses ring-width dendrochronology and provenance analysis alongside oxygen isotope dendrochronology in Belgium and its surrounding regions.The δ18O-series of the historical timbers display a high internal coherence, allowing the construction of a mean isotope series (1325 to 1468 CE). Cross-dating against master chronologies for Central England, U.K. and Fontainebleau, France, provide reliable matches that surpass statistical thresholds and quality control measures, corroborating the dating results obtained from conventional ring-width dating.Oxygen stable isotope dendrochronology emerges as a valuable tool for precise dating of historical timber structures. This pilot study demonstrates the applicability of existing reference chronologies beyond their core regions and underscores its significance in cultural heritage studies. Despite the demanding nature of the technique in terms of time and expertise, the potential benefits warrant continued investment in expanding the temporal and geographic coverage of well-replicated oxygen isotope reference chronologies

A Survey of the Current UK Physician Associate Educator Workforce and Recommendations for Courses and Provider Organizations

In the United Kingdom there are 37 physician associate (PA) training programs with limited knowledge of the educators involved, their training, and specific needs. An online questionnaire was sent to PA educators in all UK training programs requesting information on academic title and responsibilities, clinical and nonclinical background, education and qualifications before becoming a PA educator, formal and informal training received in the role, and insights into career progression. The questionnaire highlighted 5 specific areas that should be specific recommendations for UK training programs to support PA educators, alongside existing guidance. These centered on academic and leadership development, clinical engagement, student support, and pedagogical research. We believe that implementing these recommendations across training programs will improve the opportunities for all those delivering PA education and consequently improve the offering to the students undertaking PA studies programmes. [Abstract copyright: Copyright © 2024 PA Education Association.

Multi-objective Bayesian shape optimization of an industrial hydrodynamic separator using unsteady Eulerian-Lagrangian simulations

The shape of a hydrodynamic particle separator has been optimized using a parallelized and robust formulation of Bayesian optimization, with data from an unsteady Eulerian flow field coupled with Lagrangian particle tracking. The uncertainty due to the mesh, initial conditions, and stochastic dispersion in the Eulerian-Lagrangian simulations was minimized and quantified. This was then translated across to the error term in the Gaussian process model and the minimum probability of improvement infill criterion. An existing parallelization strategy was modified for the infill criterion and customized to prefer exploitation in the decision space. In addition, a new strategy was developed for hidden constraints using Voronoi penalization. In the approximate Pareto Front, an absolute improvement over the base design of 14% in the underflow collection efficiency and 10% in the total collection efficiency was achieved, which resulted in the filing of a patent.* The corresponding designs were attributed to the effective distribution of residence time between the trays via the removal of a vertical plume. The plume also reduced both efficiencies by creating a flow path in a direction that acted against effective settling. The concave down and offset tray shapes demonstrated the value of Bayesian optimization in producing useful and non-intuitive designs

Exploring South Korean Foreign Direct Investment Motives and State-Level Location Decisions: US Evidence 1995-2008

This study uses a novel application of panel fuzzy-set qualitative comparative analysis (fsQCA) in the international management field. utilizing a unique database capturing reasons for foreign direct investment (FDI), and state-level location, we explain location decisions of high-technology South Korean (henceforth Korean) multinational enterprises (MNEs), when first entering the United States of America (henceforth US), from 1995 until the 2008 financial crisis. Various home country conditions, combined with a desire for technological upgrading, encouraged firms to seek locational advantages. Additionally, rather than assuming FDI to be driven by a single purpose over time, the addition of regional characteristics allows a typology of reasons for Korean FDI to be developed. We show evolving Korean FDI trends in the US with home country and regional perspectives interacting to attract FDI into US states with different characteristics, arguing this is consistent with US policy seeking to attract inward investment to foster economic development

Enhancing Fairness, Justice and Accuracy of Hybrid Human-AI Decisions by Shifting Epistemological Stances

From applications in automating credit to aiding judges in presiding over cases of recidivism, deep-learning powered AI systems are becoming embedded in high-stakes decision-making processes as either primary decision-makers or supportive assistants to humans in a hybrid decision-making context, with the aim of improving the quality of decisions. However, the criteria currently used to assess a system’s ability to improve hybrid decisions is driven by a utilitarian desire to optimise accuracy through a phenomenon known as ‘complementary performance’. This desire puts the design of hybrid decision-making at odds with critical subjective concepts that affect the perception and acceptance of decisions, such as fairness. Fairness as a subjective notion often has a competitive relationship with accuracy and as such, driving complementary behaviour with a utilitarian belief risks driving unfairness in decisions. It is our position that shifting epistemological stances taken in the research and design of human-AI environments is necessary to incorporate the relationship between fairness and accuracy into the notion of ‘complementary behaviour’, in order to observe ‘enhanced’ hybrid human-AI decisions

A case for the use of deep learning algorithms for individual and population level assessments of mental health disorders: Predicting depression among China's elderly

Background: With the continuous advancement of age in China, attention should be paid to the mental well-being of the elderly population. The present study uses a novel machine learning (ML) method on a large representative elderly database in China as a sample to predict the risk factors of depression in the elderly population from both holistic and individual level. Methods: A total of participants met the inclusion criteria from the fourth waves of the China Health and Retirement Longitudinal Study (CHARLS) were analyzed with ML algorithms. The level of depression was assessed by the 10-item Center for Epidemiological Studies Depression Scale (CESD-10). Results: The current study found top 5 factors that were important for predicting depression in the elderly population in China, including average sleep time, gender, age, social activities and nap time during the day. The results also provide reliable diagnostic likelihood at the individual level to support clinicians identify the most impactful factors contributing to patient depression. Our findings also suggested that activities such as interacting with friends and play ma-Jong, chess or join community clubs may have a positive collaborative effect for elderly's mental health. Conclusions: Holistic approaches are an effective method of deriving and interpreting sophisticated models of mental health in elderly populations. More detailed information about a patient's demographics, medical history, sleeping patterns and social/leisure activities can help to inform policy and treatment interventions on a population and individual level. Large scale surveys such as CHARLS are effective methods for testing the most accurate models, however, further research using professional clinical input could further advance the field

Number of Publications on New Clinical Prediction Models: A Bibliometric Review

Concerns have been expressed about the abundance of new clinical prediction models (CPMs) proposed in the literature. However, the extent of this proliferation in prediction research remains unclear. This study aimed to estimate the total and annual number of CPM development-related publications available across all medical fields. Using a validated search strategy, we conducted a systematic search of literature for prediction model studies published in Pubmed and Embase between 1995 and the end of 2020. By taking random samples for each year, we identified eligible studies that developed a multivariable model (ie, diagnostic or prognostic) for individual-level prediction of a health outcome across all medical fields. Exclusion criteria included development of models with a single predictor, studies not involving humans, methodological studies, conference abstracts, articles with unavailable full text, and those not available in English. We estimated the total and annual number of published regression-based multivariable CPM development articles, based on the total number of publications, proportion of included articles, and the search sensitivity. Furthermore, we used an adjusted Poisson regression to extrapolate our results to the period 1950-2024. Additionally, we estimated the number of articles that developed CPMs using techniques other than regression (eg, machine learning). From a random sample of 10,660 articles published between 1995 and 2020, 109 regression-based CPM development articles were included. We estimated that 82,772 (95% CI 65,313-100,231) CPM development articles using regression were published, with an acceleration in model development from 2010 onward. With the addition of articles that developed non-regression-based CPMs, the number increased to 147,714 (95% CI 125,201-170,226). After extrapolation to the years 1950-2024, the number of articles increased to 156,673 and 248,431 for regression-based models and total CPMs, respectively. Based on a representative sample of publications from the literature, we estimated that nearly 250,000 articles reporting the development of CPMs across all medical fields were published until 2024. CPM development-related publications continue to increase in number. To prevent research waste and close the gap between research and clinical practice, focus should shift away from developing new CPMs to facilitating model validation and impact assessment of the plethora of existing CPMs. Limitations of this study include restriction of search to articles available in English and development of the validated search strategy prior to the popularity of artificial intelligence and machine learning models