Focal transcranial direct current stimulation (tDCS) using center-surround electrode montages enables region-specific cortical targeting, and holds promise for both cognitive neuroscience and clinical interventions. However, systematic examinations of dose-response relationships and their regional differences are lacking, hampering informed selections of suited stimulation parameters. In this preparatory methodological study, we present a modeling-based framework to support harmonized empirical dose-response studies of focal tDCS across different target areas. It covers three steps: Determining the approximate electric field strength that had led to behavioral and physiological effects in related prior tDCS studies. In our case, this led to a field strength of 0.2 V/m on average across magnetic resonance images (MRIs) from 43 participants and eight target areas related to different cognitive and motor functions. Second, optimizing the radii of center-surround montages for each target area to - on average across participants - achieve the intended field strength while maximizing focality. An additional test of cross-sample generalization in an independent sample confirms that the intended target field strength is achieved on average for new participants. Third, the pre-determined montage radii and a method for the individualized positioning of the center-surround electrode montages are provided for prospective planning in empirical dose-response studies. By harmonizing the electric field strength between different target regions at the group level, but preserving inter-individual variability, our framework will enable systematic analyses to relate the field strength to behavioral and neuroimaging outcomes, and to assess differences of these relations across regions. The described computational tools are open-source, allowing other researchers to tailor our framework to their specific research questions; and are currently used in a multi-center study involving approximately 1000 datasets.

Optimal prey choice in a daily foraging bout.

Influence prediction in collaboration networks: An empirical study on arXiv

The re-establishment of seagrass meadows following dieback events depends on the availability of viable propagules, particularly vegetative fragments that facilitate recovery beyond the local meadow through long-distance dispersal. The dispersal of vegetative fragments by ocean currents, waves and wind can be predicted by biophysical models. Among the model parameters, the duration of fragment buoyancy is an important determinant of dispersal but remains poorly quantified for tropical seagrass species. Yet, few empirical studies have assessed fragment dispersal traits and only for a small number of seagrass taxa. This limitation is particularly pronounced in tropical ecosystems, including the Great Barrier Reef (GBR), Australia, where tropical species exhibit diverse life histories and form extensive mixed-species meadows. This study aims to improve the accuracy of biophysical dispersal models for tropical seagrass by generating robust, species-specific data. We quantified the buoyancy duration of fragments from three species-Halophila ovalis, Halodule uninervis, and Zostera muelleri-over 48 days, and assessed whether initial morphological traits influenced buoyancy, finding species type was the primary determinant rather than fragment size. We then incorporated these empirical estimates into a biophysical model to evaluate their effects on dispersal. Our results highlight major differences between species. Z. muelleri floated the longest (24.7 ± 3.0 days); H. uninervis sank the fastest; and H. ovalis was intermediate, generating broken fragments available for further dispersal. Integrating these experimental derived buoyancy values into a biophysical model reduced the mean predicted dispersal distances by 44% on average compared to previous models. These findings highlight interspecific dispersal behaviours and provide useable empirical data to refine future modelling studies. Such improvements are essential for predicting seagrass recovery, guiding restoration site selection, and informing management strategies that maintain connectivity and ecosystem resilience.

A Character-level Convolutional Recurrent Interaction Network for joint traditional Chinese medicine clinical named entity recognition and relation extraction.

Dynamics and control of infectious diseases on complex networks: A theoretical and empirical study

A hybrid integer-Caputo fractional order dengue transmission model: Parameter optimization and empirical study with real-world data

How government and platform policies influence delivery riders' behavior: A theoretical and empirical study

Independent studies demonstrate that racial biases and inferences from facial appearance impact healthcare decisions, especially in pain recognition and treatment, with such biases already detectable among medical students. To address this issue, the present research evaluated the effectiveness of a multifaceted evidence-based educational intervention aimed at mitigating implicit biases by increasing students' knowledge and awareness of these factors in clinical settings and fostering strategies for equitable pain management. A total of 100 medical students were randomly assigned to an experimental or a control group. Both groups completed a pain recognition task twice, evaluating perceived pain intensity and the likelihood of recommending treatment. Between sessions, the experimental group took part in a brief educational intervention combining theoretical input on implicit biases in pain assessment, evidence from empirical studies, and applied reflection on clinical scenarios, whereas the control group received the same lesson after completing the study. The findings reveal that repeated exposure to the pain recognition task influenced responses in both groups, suggesting a task-related learning effect. The educational intervention significantly improved response times, pain intensity ratings, and treatment recommendations across stimuli categories, irrespective of race or facial trustworthiness. This suggests that the intervention heightened students' sensitivity to pain-related cues and encouraged a re-evaluation of clinical judgements. These results underscore the value of targeted educational initiatives in addressing disparities in pain recognition and treatment driven by facial cues, providing evidence that even brief interventions may contribute to mitigating implicit biases and support more equitable healthcare decision-making. This study demonstrates the effectiveness of a brief, evidence-based educational intervention in reducing implicit racial biases in pain recognition among medical students. By enhancing students' sensitivity to pain-related cues, the intervention holds promise for improving equitable healthcare practices and reducing bias-driven disparities in pain management.

Even though the ocean covers the majority of the planet’s surface, it remains the least explored ecosystem. As light and radio waves do not propagate through water, underwater acoustics is the main choice for various ocean applications ranging from marine biology to pollution monitoring. Increasing levels of anthropogenic noise from ships contribute significantly to underwater sound pollution, posing risks to marine ecosystems. This makes monitoring crucial to understand and quantify the impact of the ship radiated noise. Passive Acoustic Monitoring (PAM) systems are widely deployed for this purpose, generating years of underwater recordings across diverse soundscapes. Manual analysis of such large-scale data is impractical, motivating the need for automated approaches based on machine learning. Recent advances in automatic Underwater Acoustic Target Recognition (UATR) have largely relied on supervised learning, which is constrained by the scarcity of labeled data. Transfer Learning (TL) offers a promising alternative to mitigate this limitation. In this work, we conduct the first empirical comparative study of transfer learning for UATR, evaluating multiple pretrained audio models originating from diverse audio domains. The pretrained model weights are frozen, and the resulting embeddings are analyzed through classification, clustering, and similarity-based evaluations. The analysis shows that the geometrical structure of the embedding space is largely dominated by recording-specific characteristics. However, a simple linear probe can effectively suppress this recording-specific information and isolate ship-type features from these embeddings. As a result, linear probing enables effective automatic UATR using pretrained audio models at low computational cost, significantly reducing the need for a large amounts of high-quality labeled ship recordings. • This study presents a comparative empirical analysis of pretrained audio models for UATR. • Transfer learning reduces the need for a large labeled ship-noise datasets. • Linear probing enables effective ship-type classification from frozen audio embeddings. • Ship-type information is linearly decodable from a small subspace of the embeddings. • Pretrained audio models provide a low-cost solution for automatic UATR under minimal supervision.

Morphogenetic evolution with physical influences.

Mapping the literature on alternative food systems in the Global South. A scoping review

The relationship between cities' digitalization degrees and residents' health behaviors: An empirical study of 289 cities in China

• First empirical study of climate adaptation justice in Aotearoa New Zealand. • Adaptation is a relational process connecting people, place, and ecosystems. • Multiple justice principles are interdependent for fair climate adaptation. • Māori and multispecies perspectives are central to demands for fair adaptation. • A new framework shows justice and resilience are prerequisites for effective policy. Climate adaptation raises profound questions of fairness: Who bears the greatest risks and costs, and who decides how to respond? This study explores how communities in Aotearoa New Zealand perceive “just” climate adaptation, grounding climate justice in lived experience. We draw on 64 in-depth interviews with people who have endured floods, storms, or droughts, alongside an analysis of public submissions to national adaptation policy processes. The findings reveal that climate adaptation is widely viewed as a deeply relational process connecting people, place, and more-than-human beings. Abstract justice principles – distributive, procedural, recognitional, intergenerational, corrective, epistemic, and multispecies justice – emerge as interdependent in participants’ accounts of fair adaptation. Communities actively audit adaptation fairness through expectations of collaboration, mutual care, and government accountability. Notably, Indigenous Māori perspectives (honouring Te Tiriti o Waitangi /the Treaty of Waitangi partnership obligations) and the rights of ecosystems (legal personhood for rivers and forests) are seen as integral to just outcomes. These insights challenge universal notions of climate justice by demonstrating that what constitutes “just adaptation” is context-specific and grounded in the relationships between individuals and communities. We propose a relational framework for just adaptation that bridges theory and practice, concluding that equitable climate resilience hinges on transforming the social and ecological relationships that underlie vulnerability. Our study highlights that climate justice is not a peripheral ideal, but a crucial prerequisite for effective adaptation policy and action.

Wine is prone to food fraud vulnerability due to its intricate supply chain and high price. Identification technologies attached to wine products are considered to contribute to their authenticity. This view has obtained a wide consensus in the wine sector, but not all the products have implemented the identification technologies in business practice. There is a lack of empirical studies on drivers and barriers to identification technology implementation since the perceptions of the wine supply chain actors are unclear. Importantly, how to promote the drivers and mitigate the barriers is still unknown. In this study, the interviews were conducted with grape growers, winemakers, bottlers, and distributors from Italy and India to explore the drivers, barriers, and strategies influencing the implementation of identification technology in two diverse supply chains. The findings show that the drivers for implementing identification technologies are efficient traceability, consumer trust, regulatory compliance, brand enhancement, ease of use, counterfeiting risks, counterfeiting monitoring, and keeping competitive; the barriers include implementation costs, traditional mindset, antiquated production lines, lacking technicians, forged devices, no counterfeiting, low visibility, and unequal returns; the strategies are collaboration enhancement, cost optimization, regulatory support, multi-technology integration, simplified systems, raising awareness, and training programs. The results of this research can improve the identification technology utilities in the Italian and Indian wine sectors to enhance the supply chain integrity. • Wine supply chain actors in Italy and Indian gave key views on technology use. • New insights were generated into the drivers and barriers for wine authentication. • Strategies were identified to overcome the barriers and ensure wine authenticity.

• Synthetic data from generative AI preserves privacy in healthcare modeling. • Generative AI adapts dynamically, surpassing static traditional CEA models. • Enhanced scenario simulations by generative AI aid robust decision-making. • Generative AI integrates real-world evidence, refining predictive accuracy. • Non-linear modeling in AI captures complex healthcare cost-outcome relations. Healthcare economic evaluation increasingly relies on predictive modeling to inform resource allocation decisions. Traditional cost-effectiveness analysis (CEA) methodologies face significant challenges when processing complex, heterogeneous healthcare datasets and accommodating dynamic system variables. This review examines how generative artificial intelligence technologies may transform predictive modeling frameworks in healthcare economics, specifically focusing on potential improvements in accuracy, adaptability, and efficiency in cost-effectiveness analyses. A literature search was conducted across PubMed, Scopus, Web of Science, and IEEE Xplore between October 2024 and January 2025, examining publications from 2018-2024. Critically, we identified a near absence of empirical studies that directly apply and validate generative AI technologies within formal health economic modeling or health technology assessment contexts. Most identified literature addresses general AI/ML applications in healthcare or synthetic data generation in adjacent domains, rather than demonstrating validated use in cost-effectiveness analysis. Generative AI demonstrates promising theoretical capabilities in handling non-linear healthcare relationships, generating privacy-preserving synthetic datasets, and enabling dynamic scenario exploration based on performance in related fields. However, direct empirical evidence comparing generative AI to traditional CEA approaches in real-world health technology assessment remains virtually non-existent. Potential advantages include automated model support, enhanced integration of real-world evidence, and improved handling of missing data scenarios. Technologies such as Generative Adversarial Networks and Variational Autoencoders show early-stage promise in addressing traditional modeling limitations in adjacent applications. Generative AI represents a conceptually significant potential advancement in healthcare economic modeling. However, claims presented are predominantly forward-looking and conceptual rather than empirically validated. Implementation challenges including model interpretability, regulatory frameworks, validation requirements, and ethical considerations require substantial empirical research before successful integration into healthcare decision-making processes.

What influences product designers’ use of large language models? An empirical study based on a SEM-ANN hybrid model

The association between testosterone and risk-taking behavior has been widely investigated across behavioral economics, neuroendocrinology, and social neuroscience, but empirical results remain inconsistent. To clarify this relationship, we conducted a multilevel random-effects meta-analysis of 52 empirical studies (94 independent effect sizes; total N = 17,340), the most comprehensive so far, examining correlations between testosterone levels or manipulations and risk preferences across diverse paradigms. The aggregated effect was statistically null (r = -0.0021, 95% CI [-0.0431, 0.0389], p = .919), indicating no reliable link between testosterone and risk-taking. Publication bias diagnostics (trim-and-fill and fail-safe N) suggested that this null effect is not driven by selective reporting. Meta-regressions revealed significant heterogeneity across testosterone measurement type. Moreover, only lottery-based economic tasks showed a modest positive association, whereas other paradigms (e.g., BART, IGT, self-report) did not. A separate meta-analysis of sex differences found no moderating effect, suggesting that testosterone-risk correlations are not reliably stronger in males than females. Overall, the evidence challenges the notion that testosterone provides a general hormonal basis for human risk preferences. Instead, findings support a biopsychosocial framework in which "risk taking" reflects the interaction of task demands, cognitive-affective processes, and situational context, with endocrine effects appearing narrow, context-dependent, and method-specific. Future work should employ preregistered, multi-measure designs and direct endocrine assays to test mechanistic pathways more precisely.

This scoping review examines how physical education teacher education faculty members are socialized into and through academic roles across their careers. Drawing upon an adapted version of occupational socialization theory that includes anticipatory socialization, academic career preparation, and faculty socialization, the review synthesizes findings from 37 empirical studies published between 1991 and 2023. Key trends in publication, authorship, methodology, and conceptual framing are identified, and persistent gaps are highlighted. Most studies focused on early-career faculty members, with limited attention to formative stages of academic identity or late-career transitions. Findings underscore the influence of mentorship, institutional structures, and identity-based experiences on professional development. Despite growing interest, the literature remains methodologically narrow and disproportionately shaped by a small number of scholars and institutions. This review calls for greater conceptual clarity, methodological diversity, attention to social identity, and structural influences to better support faculty preparation and development in physical education teacher education.

Mechanisms of environmental stimuli on learning efficacy: An empirical study of university library spaces