Wind power has been at the forefront of renewable energy investment, but bird fatalities from turbine collisions remain a key ecological and social concern. Increasingly, how principles from sensory ecology might reduce collisions by enhancing the detectability or aversiveness of turbine blades have been investigated. In nature, aposematic species use high-contrast colors and striped patterns to warn predators of their unprofitability. These signal elements are effective due to their conspicuousness across variable natural scenes, memorability, generalisability from mimicry, and exploitation of innate color aversions. This begs the question: might employing biologically inspired turbine warning colors help birds to avoid turbine blades? Here, we used a screen-based "game" experimental setup to test the behavioral responses of wild-caught great tits (Parus major) to 3 existing wind turbine patterns (white, red-striped, and single black blade) as well as a novel biologically inspired aposematic pattern. Tits were less likely to approach and, when they did approach, took significantly longer to approach patterned than uniform white blades. This effect was strongest for our bio-inspired pattern. Our work supports the need for further investigation into the use of warning patterns to reduce bird collisions with wind turbines.
Aposematism has evolved as a strategy to warn predators of potential danger. While visual signals have received considerable attention, acoustic signals remain less studied despite their flexibility across contexts and individuals. We investigated defensive behaviours (hissing and biting) in the nose-horned viper (Vipera ammodytes). First, we assessed the repeatability of defensive behaviours under standardized conditions. Second, we quantified responses in a test arena simulating increasing threat levels: (i) no stimulation, (ii) visual stimulation, and (iii) combined visual and tactile stimulation. Hissing and biting were highly repeatable within individuals but varied markedly among them. Males showed a higher likelihood of exhibiting defensive behaviours than females. In the test arena, hissing and its acoustic variables (duration, intensity) increased with threat level. Biting occurred only during the visual and combined stimulation phases, and its frequency increased between the two periods. Consistent with the hypothesis of quantitative signal honesty, we found that hissing characteristics (number of hisses and hiss intensity) were positively correlated with the number of bites. Overall, our results reveal that defensive behaviour expression is influenced by personality traits, sex and threat level, highlighting the complexity of acoustic warning signals in snakes and their potential role as honest indicators of defensive capacity.
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to identify the obstacles in the work process and the strategies used by leaders in supporting healthcare workers involved in incidents related to psychosocial risks. an exploratory, descriptive, and qualitative study conducted between January 2023 and September 2024, with ten leaders at a university hospital in southern Brazil. Data were collected from records of the occupational health and psychology services, the human resources department, the institution's strategic operational management software, as well as from semi-structured interviews, which were subjected to Thematic Content Analysis. twenty-three psychosocial incidents with repercussions for workers were identified. Two categories emerged from the interviews: Worker support; focusing on the challenges and strategies for its effective implementation, and Participatory management, aimed at building environments that promote worker's involvement and shared responsibility. the main factors that interfere with the effective support to healthcare workers following incidents involving psychosocial risks. The strategies adopted by leadership, although present, require improvement and greater systematization to ensure adequate psychosocial support, favoring the promotion of the team's health and safety.
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This study examines the relation between political uncertainty arising from state-level election cycles and the timing of employee dismissal and plant closure notices filed by US firms under the Worker Adjustment and Retraining Notification (WARN) Act of 1988 (hereafter, WARN notices). We appeal to a real options framework to predict that firms delay layoff decisions and the issuance of WARN notices until the resolution of political uncertainty. Using establishment-level data on layoffs disclosed in WARN notices and state elections occurring between 1994 and 2022, we document that the likelihood of issuing WARN notices declines during the election quarter but increases in the subsequent quarter. Cross-sectional findings show that political uncertainty plays a significant role in the timing of WARN notices during election periods while other factors, including partisanship, economic conditions, union strength, and firm visibility, may also play a role. Further, firms that delay WARN notices do not experience a significant deterioration in their medium-term financial performance. Overall, our findings provide evidence that firms delay labor adjustment decisions and the announcements of such decisions in response to political uncertainty.
The Hong Kong Observatory has operated the Very Hot Weather Warning since 2000 to warn the local population of very hot weather, defined as when the daily maximum temperature (Tmax) reaches 33 °C. Recent years have seen an increasing number of days with Tmax reaching 35 °C, hence necessitating a review and update of the warning system. In this study, the health impacts of heat were assessed to inform and support the update of the Very Hot Weather Warning. A retrospective study of heat-health related mortality and emergency calls of the Hong Kong population in 2012 to 2022 was conducted. Generalized linear models and distributed lag non-linear model were performed to estimate relative risk (RR) regarding different levels of Tmax. Sensitivity analyses were performed with other indicators of heat exposure. There was a dip of RR at Tmax starting from around 34 °C for cardiovascular disease and heat stroke emergency calls for those aged 0 to 64, although not reaching statistical significance. Among those aged 0 to 64, the RR for heat-health related mortality and emergency calls increased to 1.148 (95% CI: 0.832 to 1.584) and 1.074 (95% CI: 0.984, 1.171) at Tmax of 35.5 °C, respectively. In terms of lag effect, it was found that the lag effect of high temperatures on emergency call was within 1 day. Among alternative heat exposure indicators, daily minimum temperature (Tmin) consistently demonstrated elevated risk in extreme hot temperature for all the outcomes. This observational study of association between adverse health outcomes and extreme high temperature supports the addition of an advisory message when Tmax reaches 35 °C for Hong Kong. Further enhancement may involve other heat stress indicator.
Generative artificial intelligence (AI) technologies might offer new possibilities for the peer review process; however, AI models' possible vulnerability to hidden nudges designed to elicit positive reviews raises concerns about manipulation susceptibility, which remains unexplored. We aimed to evaluate AI model susceptibility to hidden nudges in peer review. This quasi-experimental study was conducted between July and December 2025. Four commercial AI models were evaluated simultaneously: GPT-4 (OpenAI), Gemini 2.5 Flash (Google), DeepSeek-V3 (DeepSeek), and Claude Opus 4 (Anthropic). We used 90 pre-print and 90 published manuscripts in critical care and cardiology to feed the AI models. All manuscripts were converted to individual Microsoft Word files, with identifying information removed, to mimic a manuscript submitted to a journal for peer review. Each manuscript underwent three independent evaluations per model using standardized prompts requesting evaluation and recommendation on whether to accept or reject it for publication. First, we evaluated the manuscript without any nudge. Second, we inserted a hidden nudge opposing the initial recommendation (e.g., a negative nudge if initially accepted). Finally, we evaluated the nudged manuscripts using a modified prompt warning about potential hidden nudges. All recommendations were categorized as accept or reject. The main outcomes were the change rates in recommendations after nudge insertion compared to initial recommendations, and after nudge insertion with the modified prompt, analyzed separately for each AI model. Across all AI models tested, nudge insertion led to a change in the recommendation in 84.4% of the time (608/720), with Deepseek being the most susceptible model (100% of change), followed by Gemini (97.8% of change), Chat GPT (82.8% of change) and Claude (57.2% of change). Using a specific prompt to warn AI models about potential malicious nudge injections in the manuscripts did not substantially alter the results. Recommendations were still modified in 76.8% of cases (553/720). In this quasi-experimental study, all tested AI models were highly susceptible to hidden nudge insertions in manuscripts during simulated peer review. Importantly, explicitly warning AI models about potential nudge injections does not meaningfully reduce their susceptibility to manipulation.