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We investigate the variability of the power emission of Io's hotspots by using recent Juno/JIRAM infrared observations. The Jovian Infrared Auroral Mapper (JIRAM) is an imaging spectrometer which began observing Jupiter in August 2016. Although observing Jupiter's moons is not its primary objective, JIRAM can use the frequent opportunities to observe Io (up to once per orbit) to gather infrared images and spectra of its surface. The present study uses the data acquired by JIRAM during the last 2 years, including the location and morphology of Io's hotspots, and the temporal variability of the total output. A new photometric model for the hotspots and the dayside surface has been developed, which permits us to disentangle the temporal variability from the changes in the observation geometry. While the latitudinal dependence of the power output is not well constrained, low-latitude hotspots show a significantly more intense temporal variability and greater temperature. [ABSTRACT FROM AUTHOR]

Juno has allowed clear, high‐resolution imaging of Io's polar volcanoes using the Jovian Infrared Auroral Mapper (JIRAM) instrument. We have used data from JIRAM's M‐band (4.78 μm) imager from 11 Juno orbits to construct a global map of volcanic flux. This map provides short‐term insight into the spatial distribution of volcanoes and the ways in which high‐ and low‐latitude volcanoes differ. Using spherical harmonic analysis, we quantitatively compare our volcanic flux map to the surface heat flow distribution expected from models of Io's tidal heat deposition (summarized in de Kleer, Park, et al. (2019, https://doi.org/10.26206/d4wc‐6v82). Our observations confirm previously detected systems of bright volcanoes at high latitudes. Our study finds that both poles are comparably active and that the observed flux distribution is inconsistent with an asthenospheric heating model, although the south pole is viewed too infrequently to establish reliable trends. Plain Language Summary: Our study uses data from an infrared camera on Juno called the Jovian Infrared Auroral Mapper (JIRAM) to image Io, the innermost Galilean moon of Jupiter. Io is the most volcanically active body in the Solar System. Io's volcanoes are powered by both the extreme tides from Jupiter and the gravitational interactions between it and Jupiter's other moons. These tides generate friction inside Io. Simulations of Io's interior suggest that, depending on how deep that friction is being generated, the surface heat flow will be higher in certain areas (de Kleer, Park, et al., 2019, https://doi.org/10.26206/d4wc‐6v82). Using JIRAM, we have mapped where volcanoes are producing the most power and compared that to where we expect higher heat flow from the interior models. Our map doesn't agree with any of these models very well. JIRAM observed more volcanic activity at the poles than we expected to see based on previous observations. However, since the south pole was only observed twice, it's possible that these observations don't represent the average volcanic activity of the south pole. Very bright volcanoes that may have been continuously active for decades were also imaged during these Juno fly‐bys, some of which are nearer the poles than the equator. Key Points: We have produced a global volcanic flux map of Io using data from 11 Juno fly‐bysOur flux map doesn't agree well with tidal heat flow models reported in de Kleer, Park, et al. (2019, https://doi.org/10.26206/d4wc‐6v82)The M‐band flux is anti‐correlated with the asthenospheric heating model and has only very weak agreement with the global magma ocean model [ABSTRACT FROM AUTHOR]

The document is a corrigendum for an article on Io's hotspots, correcting references to various studies and authors. The corrections ensure accurate citations and do not impact the scientific conclusions of the original article. The authors are from various institutions in Italy, the United States, and other countries, emphasizing international collaboration in space research. [Extracted from the article]

Introduction: Over the past few years, Psychiatry has undergone a significant transformation with the integration of Artificial Intelligence (AI). This shift has been driven by the increasing demand for mental health services, as well as advances in AI technology. AI analyzes extensive datasets, including text, voice, and behavioral data, aiding in mental health diagnosis and treatment. Consequently, a range of AI-based interventions has been developed, including chatbots, virtual therapists and apps featuring cognitive-behavioral therapy (CBT) modules. Notably, chatbots, as conversational agents, have emerged as valuable tools, assisting users in monitoring emotions and providing evidence-based resources, well-being support, psychoeducation and adaptive coping strategies. Objectives: This study aims to investigate the impact of AI chatbots on improving mental health, evaluate their strengths and weaknesses and explore their potential for early detection and intervention in mental health issues. Methods: A literature review was conducted through PubMed and Google Scholar databases, using keywords 'artificial intelligence', 'chatbot' and 'mental health'. The selection focused on the most relevant articles published between January 2021 and September 2023. Results: Mental health chatbots are highly personalized, with a primary focus on addressing issues such as depression or anxiety within specific clinical population groups. Through the integration of Natural Language Processing (NLP) techniques and rule-based AI algorithms, these chatbots closely simulate human interactions and effectively instruct users in therapeutic techniques. While chatbots integrating CBT principles have gained widespread use and extensive research attention, some also incorporate alternative therapeutic approaches, including dialectical behavior therapy, motivational interviewing, acceptance and commitment therapy, positive psychology or mindfulness-based stress reduction. AI chatbots provide substantial advantages in terms of accessibility, cost-effectiveness and improved access to mental health support services. Nonetheless, they also exhibit limitations, including the absence of human connection, limited expertise, potential for misdiagnosis, privacy concerns, risk of bias and limitations in risk assessment accuracy. Conclusions: AI-based chatbots hold the potential to enhance patient outcomes by enabling early detection and intervention in mental health issues. However, their implementation in mental health should be approached with caution. Further studies are essential to thoroughly evaluate their effectiveness and safety. Disclosure of Interest: None Declared [ABSTRACT FROM AUTHOR]

Introduction: Involuntary admission rates differ between gender across various countries. In several European Union countries, men are more frequently involuntarily admitted, while an opposite trend, associating women with involuntary care, has been observed in countries like Switzerland, Brazil, and China. Objectives: Considering the contradictory evidence about gender and involuntary care in the literature, we aim to analyze the gender patterns of involuntary care in Centro Hospitalar Médio Tejo's Psychiatric Acute Unit, exploring the gender differences in diagnosis among involuntary patients. Methods: We stored and analyzed the data using Microsoft Excel and IBM SPSS Statistics. We studied psychiatry admissions at Centro Hospitalar Médio Tejo, Portugal over 2 years. The Acute Psychiatric Unit, located within a general hospital, has 24 beds, and offers acute mental healthcare services to adults aged 18 and above, serving a coverage area of approximately 251,000 residents. As part of our data collection process for all admissions to the Acute Psychiatry Unit, we recorded information such as gender, age, diagnosis at discharge, treatment type (voluntary or involuntary), and length of stay. Results: From January 1, 2021, to December 31, 2022, there were 686 psychiatry admissions at Centro Hospitalar Médio Tejo, of which 125 (18,2%) were involuntary. The admission rates were approximately 136.6 per 100,000 people annually, with 24.9 being involuntary admissions per 100,000 people annually. In our analysis of involuntary admissions, women had a lower rate of such admissions, making up 6.4%, while men had a higher rate at 11.8%. No other gender identity was mentioned. Schizophrenia-related disorders were the primary cause for involuntary admissions for both genders, with 67.9% for men and 50% for women. Mood disorders were the second most common reason for involuntary admission, accounting for around 40.9% of cases for women and a significantly lower 16% for men. Involuntarily hospitalized patients exhibited longer lengths of stay independently of the gender. Men hospitalized involuntarily tended to be younger, whereas for women, involuntary hospitalizations were associated with older ages. Conclusions: In conclusion, our study reveals gender differences in psychiatric involuntary admissions, with more men being involuntarily admitted than women. Schizophrenia group disorders were the most common diagnoses among male and female involuntary patients. Furthermore, all hospitalized women exhibited a higher prevalence of mood disorders, a trend that was more pronounced among those admitted involuntarily. These gender trends match the overall patterns seen in the epidemiology of schizophrenia and mood disorders. Additionally, women with schizophrenia generally exhibit better social functioning than men, which may explain the lower needs of involuntary hospitalization. Disclosure of Interest: None Declared [ABSTRACT FROM AUTHOR]