Nature-based solutions are becoming increasingly recognized as effective tools for addressing various environmental problems. This study presents a novel approach to selecting optimal blue–green infrastructure (BGI) solutions tailored to the unique environmental and climatic challenges of Istanbul, Türkiye. The primary objective is to utilize a Bayesian Belief Network (BBN) model for assisting in the identification of the most effective BGI solutions, considering the city’s distinct environmental conditions and vulnerabilities to climate change. Our methodology integrates comprehensive data collection, including meteorological and land use data, and employs a BBN model to analyze and weigh the complex network of factors influencing BGI suitability. Key findings reveal the model’s capacity to effectively predict BGI applicability across diverse climate scenarios, with quantitative results demonstrating a notable enhancement in decision-making processes for urban sustainability. Quantitative results from our model reveal a significant improvement in decision-making accuracy, with a predictive accuracy rate of 82% in identifying suitable BGI solutions for various urban scenarios. This enhancement is particularly notable in densely populated districts, where our model predicted a 25% greater efficiency in stormwater management and urban heat island mitigation compared to traditional planning methods. The study also acknowledges the limitations, such as data scarcity and the need for further model refinement. The results highlight the model’s potential for application in other complex urban areas, making it a valuable tool for improving urban sustainability and climate change adaptation. This study shows the importance of incorporating detailed meteorological and local climate zones data into urban planning processes and suggests that similar methodologies could be beneficial for addressing environmental challenges in diverse urban settings.

Suicide is one of the leading causes of death among individuals aged 10–24. Research using intensive longitudinal methods to identify near-term predictors of suicidal thoughts and behaviors (STBs) has grown dramatically. Interpersonal factors may be particularly critical for suicide risk among young people, given the heightened salience of interpersonal experiences during adolescence and young adulthood. We conducted a narrative review on intensive longitudinal studies investigating how interpersonal factors contribute to STBs among adolescents and young adults. Thirty-two studies met the inclusion criteria and focused on theoretical and cross-theoretical interpersonal risk factors. Negative interpersonal states (e.g., perceived burdensomeness), hopelessness, and social support were consistently associated with proximal within-person changes in concurrent, but not prospective, suicidal thoughts. Further, work examining how these processes extend to suicidal behavior and among diverse samples remains scarce. Implications for contemporary interpersonal theories and intensive longitudinal studies of STBs among young people are discussed.

This article presents the first complete biography in English of the early hadith critic al-Jūzjānī (d. 259/873?), in addition to a thorough analysis of his work Aḥwāl al-rijāl, the earliest Syngramma dedicated to the genre of al-jarḥ wa-l-taʿdīl. Through a detailed examination of al-Jūzjānī's engagement with the opinions of earlier hadith critics, his use of the terms of hadith criticism and his own remarks, this article delineates his conception of the function of hadith, methodological framework and approach to the appraisal of hadith transmitters, arguing that al-Jūzjānī may have been the first and only hadith scholar to methodically incorporate the consideration of transmitters’ conformity to the “correct” doctrines in hadith criticism. His methodological innovation, however, departs from existing convention among ahl al-ḥadīth. As a result, although al-Jūzjānī's authority as a hadith critic was well recognized, his approach failed to appeal to succeeding contributors to hadith criticism.

This article addresses the challenges of assessing pedestrian-level wind conditions in urban environments using a deep learning approach. The influence of large buildings on urban wind patterns has significant implications for thermal comfort, pollutant transport, pedestrian safety, and energy usage. Traditional methods, such as wind tunnel testing, are time-consuming and costly, leading to a growing interest in computational methods like computational fluid dynamics (CFD) simulations. However, CFD still requires a significant time investment for such studies, limiting the available time for design modification prior to lockdown. This study proposes a deep learning surrogate model based on a MLP-mixer architecture to predict mean flow conditions for complex arrays of buildings. The model is trained on a diverse dataset of synthetic geometries and corresponding CFD simulations, demonstrating its effectiveness in capturing intricate wind dynamics. The article discusses the model architecture and data preparation and evaluates its performance qualitatively and quantitatively. Results show promising capabilities in replicating key wind features with a mean error of 0.3 m/s and rarely exceeding 0.75 m/s, making the proposed model a valuable tool for early-stage urban wind modelling.

Comprehensive housing stock information is crucial for informing the development of climate resilience strategies aiming to reduce the adverse impacts of extreme climate hazards in high-risk regions like the Caribbean. In this study, we propose an end-to-end workflow for rapidly generating critical baseline exposure data using very high-resolution drone imagery and deep learning techniques. Specifically, our work leverages the segment anything model (SAM) and convolutional neural networks (CNNs) to automate the generation of building footprints and roof classification maps. We evaluate the cross-country generalizability of the CNN models to determine how well models trained in one geographical context can be adapted to another. Finally, we discuss our initiatives for training and upskilling government staff, community mappers, and disaster responders in the use of geospatial technologies. Our work emphasizes the importance of local capacity building in the adoption of AI and Earth Observation for climate resilience in the Caribbean.

In situ glaciological observations in the Himalaya–Karakoram (HK) region mostly come from small glaciers. Drang Drung (69.6 km2, Zanskar, Ladakh) is the largest glacier in the HK monitored for in situ glacier-wide mass balances applying the traditional glaciological method. During 2021–23, point ablation varies from –1.8 to –8.3 meter water equivalent (m w.e. a–1) in the ablation area, and from 0.15 to 1.70 m w.e. a–1 in the accumulation area. The mean glacier-wide mass balance is −0.74 ± 0.43 m w.e. a−1 over 2021‒2023, corresponding to a mean equilibrium line altitude of 5134 m a.s.l. and accumulation area ratio of 0.53. The mean annual vertical mass-balance gradient of 0.62 m w.e. (100 m)–1 on Drang Drung Glacier resembles that observed on other Himalayan glaciers. These initial investigations on Drang Drung Glacier address the gap for glacier monitoring in the Zanskar Range and will be continued in the long term.

Indigenous peoples are often not routinely included in iodine programmes because of language barriers and remote access and may thus be at higher risk of iodine deficiency disorders, which could adversely impact their quality of life. We conducted this cross-sectional study in the remote Pwo Karen community of Thailand to determine the urinary iodine concentration of school-aged children and women of reproductive age and investigate the iodine content in household salt. We measured urinary iodine concentration in spot urine samples from healthy school-aged children and women of reproductive age, administered a questionnaire, estimated daily iodine intake and collected household salt samples to determine salt iodine concentration. The median urinary iodine concentration (range) of school-aged children (n 170) was 192 (136–263) µg/l, which was significantly higher than women of reproductive age (n 306) (147 (89–233) µg/l) (P < 0·001). The estimated daily iodine intake in school-aged children and women of reproductive age were 135 and 195 μg/d, respectively. The median (range) iodine concentration in rock and granulated salts consumed in the households were 2·32 (0·52–3·19) and 26·64 (20·86–31·01) ppm, respectively. Surprisingly, the use of iodised salt and the frequency of seafood consumption were NS predictors of urinary iodine concentration in these two groups. Our data suggest that school children and women of the Pwo Karen community have sufficient iodine intake, indicating the Thai salt iodisation programme is effectively reaching even this isolated Indigenous community. Sentinel surveys of remote vulnerable populations can be a useful tool in national iodine programmes to ensure that programme coverage is truly universal.

A description is provided of the current situation in Aotearoa New Zealand with regard to compulsory treatment of people with schizophrenia. This is placed within the context of homelessness in New Zealand and the provision of services to the incarcerated mentally ill. There are high rates of homelessness and incarceration and services are struggling to meet their needs. This is particularly a problem for the indigenous population. The current Mental Health Act allows for compulsory treatment of people who as a result of schizophrenia are seriously impaired in their capacity to care for themselves, and this will include people where there is a nexus between homelessness and their illness. The Mental Health Act is being reformed, with a new act likely to emphasize autonomy and capacity to a greater degree. Finally, the author considers the learnings from 5 years working within the Fixated Threat Assessment Centre, which provides a unique perspective on these issues.

High-resolution simulations such as the ICOsahedral Non-hydrostatic Large-Eddy Model (ICON-LEM) can be used to understand the interactions among aerosols, clouds, and precipitation processes that currently represent the largest source of uncertainty involved in determining the radiative forcing of climate change. Nevertheless, due to the exceptionally high computing cost required, this simulation-based approach can only be employed for a short period within a limited area. Despite the potential of machine learning to alleviate this issue, the associated model and data uncertainties may impact its reliability. To address this, we developed a neural network (NN) model powered by evidential learning, which is easy to implement, to assess both data (aleatoric) and model (epistemic) uncertainties applied to satellite observation data. By differentiating whether uncertainties stem from data or the model, we can adapt our strategies accordingly. Our study focuses on estimating the autoconversion rates, a process in which small droplets (cloud droplets) collide and coalesce to become larger droplets (raindrops). This process is one of the key contributors to the precipitation formation of liquid clouds, crucial for a better understanding of cloud responses to anthropogenic aerosols and, subsequently, climate change. We demonstrate that incorporating evidential regression enhances the model’s credibility by accounting for uncertainties without compromising performance or requiring additional training or inference. Additionally, the uncertainty estimation shows good calibration and provides valuable insights for future enhancements, potentially encouraging more open discussions and exploration, especially in the field of atmospheric science.

Edge AI is the fusion of edge computing and artificial intelligence (AI). It promises responsiveness, privacy preservation, and fault tolerance by moving parts of the AI workflow from centralized cloud data centers to geographically dispersed edge servers, which are located at the source of the data. The scale of edge AI can vary from simple data preprocessing tasks to the whole machine learning stack. However, most edge AI implementations so far are limited to urban areas, where the infrastructure is highly dependable. This work instead focuses on a class of applications involved in environmental monitoring in remote, rural areas such as forests and rivers. Such applications have additional challenges, including failure proneness and access to the electricity grid and communication networks. We propose neuromorphic computing as a promising solution to the energy, communication, and computation constraints in such scenarios and identify directions for future research in neuromorphic edge AI for rural environmental monitoring. Proposed directions are distributed model synchronization, edge-only learning, aerial networks, spiking neural networks, and sensor integration.

Lifestyle and diet may affect the reproductive cycle. A dietary index called Diet Diversity Score (DDS) may be related to various reproductive outcomes. The present review aims to look over and conclude the prior studies on the relationship between the diversity of food ingredients and issues related to reproductive health and pregnancy. In the case of this relationship, our findings can increase clinical knowledge and help recommend a well-balanced diet for the target group. A comprehensive search was performed in major databases such as PubMed, Google Scholar, Web of Science, Scopus, and Scientific Information Database until March 2024. This research was combined with a search of Elsevier and SpringerLink databases, which led to the inclusion of relevant articles in this review. Our study was conducted based on 27 articles from 2012 to 2023, all containing a possible link between dietary diversity and reproductive complications. The Newcastle-Ottawa Scale quality assessment was used to evaluate the quality of included studies. Due to our results, a higher score in DDS, which led to an increased intake of major nutrients and a greater variety of foods, was correlated with a lower risk of reproductive health disorders such as polycystic ovary syndrome, maternal anaemia, and maternal bone status, as well as a reduced likelihood of certain birth outcomes, including low-birth weight infants, Apgar score and congenital heart defect. These findings highlight the importance of improving the DDS for maternal and infant health.

Legal experts—lawyers, judges, and academics—typically resist changing their beliefs about what the law is or requires when they encounter disagreement from those committed to different jurisprudential or interpretive theories. William Baude and Ryan Doerfler are among the most prominent proponents of this view, holding it because fundamental differences in methodological commitments severs epistemic peerhood. This dominant approach to disagreement, and Baude and Doerfler’s rationale, are both wrong. The latter is committed to an overly stringent account of epistemic peerhood that dogmatically excludes opponents. The former violates the conjunction of three plausible epistemic principles: Complete Evidence, considering all epistemically permissible evidence; Independence, in which only dispute-independent evidence is epistemically permissible; and Peer Support, which involves epistemically permissible evidence. Instead, I argue for jurisprudential humility—we ought to be more willing to admit we do not know what the law is or requires, and take seriously conflicting views.

Hong Kong is an intermediate tuberculosis (TB) endemicity city dominated by reactivation diseases. A cross-sectional study on the clinical and epidemiologic data of newly diagnosed TB cases was conducted in such a setting, to examine the association between ambient PM2.5 and TB reactivation. After the exclusion of cases most likely resulting from recent infection, four distinct TB population phenotypes were delineated by latent class analysis based on their reactivation risk and clinical profiles (N = 2,153): ‘Elderly male’ (26%), ‘Otherwise healthy younger adult’ (34%), ‘Older female’ (19%) and ‘Male smoker’ (21%). Overall, exposure to high concentrations of ambient PM2.5 6 and 12 months before the notification was significantly associated with ‘Otherwise healthy younger adults’ membership (OR = 1.07 and 1.11, respectively) compared with ‘Elderly male’. Such association was less evident for other phenotypes. The differential pattern of association between ambient PM2.5 exposure and TB population phenotypes suggested the role of ambient PM2.5 in TB reactivation.

The COVID-19 pandemic laid bare the inequities in U.S. healthcare in ways that captured public attention and reinforced the need to view all of healthcare through an equity lens. It also exposed global inequities in access to healthcare technologies. At Rockefeller University, we participate in the entire spectrum of translational research, but our focus is in the areas of basic research and new methods to prevent, diagnose, and treat disease, extending to proof of concept preclinical and Phase 1 studies. Since we believe that all phases of translational research should have an equity lens, we have instituted an initiative to encourage thought and planning about global equitable access to discoveries made by our trainee Clinical Scholars and faculty, even at the earliest phases of basic research. Assuring global equitable access to new technologies requires addressing at least 3 different aspects of new technology: 1. Patenting and licensing, 2. Manufacturing, and 3. Dissemination and implementation in low- and middle-income countries. In this review, I focus on patenting and licensing and offer ten questions for inventors to consider in discussing licensing their technologies with technology transfer officers to maximize equitable global access to the technologies they create.