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In January 2025, the nightly census revealed that over 120,000 people were staying in New York City (NYC) shelters, including more than 41,000 children, of whom almost half were aged 0-5 years. Children under five years old (under-5s) experiencing homelessness are especially vulnerable because the first five years of life are a critical period for child growth, including approximately 90% of brain development. Furthermore, under-5s experiencing homelessness have a higher risk for multiple adverse childhood experiences, developing chronic health conditions, and recurrent homelessness across the life course. Data available for under-5s experiencing homelessness is generally lacking, and what is available is of notably poor quality in the United States, leaving a wide evidence gap and an inability to determine the actual needs of this population. This proposed protocol employs community-based participatory research and was co-developed with families with under-5s who have lived experience of homelessness in NYC shelters. The aim is to determine what barriers exist in the physical and social environments to optimizing health and wellbeing (e.g., milestones, child mental health, parental mental health, safety) among under-5s living in NYC shelters. Using a sequential mixed-methods design, we propose to address a gap in the current literature by conducting an assets- and deficits-based health needs assessment comprising a quantitative survey and qualitative semi-structured interviews. In the long term, our objective is to enhance the quality and quantity of data for this vulnerable population, thereby laying the groundwork for the future co-development of a comprehensive, optimized intervention addressing the needs of under-5s experiencing homelessness.

This study accounted for and analyzed the carbon emissions of 13 cities in Jiangsu Province from 1999 to 2021. We compared the simulation effects of four models—STIRPAT, random forest, extreme gradient boosting, and support vector regression—on carbon emissions and performed model optimization. The random forest model demonstrated the best simulation performance. Using this model, we predicted the carbon emission paths for the 13 cities in Jiangsu Province under various scenarios from 2022 to 2040. The results show that Xuzhou has already achieved its peak carbon target. Under the high-speed development scenario, half of the cities can achieve their peak carbon target, while the remaining cities face significant challenges in reaching their peak carbon target. To further understand the factors influencing carbon emissions, we used the machine learning interpretation method SHAP and the features importance ranking method. Our analysis indicates that electricity consumption, population size, and energy intensity have a greater influence on overall carbon emissions, with electricity consumption being the most influential variable, although the importance of the factors varies considerably across different regions. Results suggest the need to tailor carbon reduction measures to the differences between cities and develop more accurate forecasting models.

Understanding factors that influence a species' distribution and abundance across the annual cycle is required for range-wide conservation. Thousands of imperiled red knots (Calidris cantus rufa) stop on Virginia's barrier islands each year to replenish fat during spring migration. We investigated the variation in red knot presence and flock size, the effects of prey on this variation, and factors influencing prey abundance on Virginia's barrier islands. We counted red knots and collected potential prey samples at randomly selected sites from 2007-2018 during a two-week period during early and peak migration. Core samples contained crustaceans (Orders Amphipoda and Calanoida), blue mussels (Mytilus edulis), coquina clams (Donax variabilis), and miscellaneous prey (horseshoe crab eggs (Limulus polyphemus), angel wing clams (Cyrtopleura costata), and other organisms (e.g., insect larvae, snails, worms)). Estimated red knot peak counts in Virginia during 21-27 May were highest in 2012 (11,959) and lowest in 2014 (2,857; 12-year peak migration x[MACRON] = 7,175, SD = 2,869). Red knot and prey numbers varied across sampling periods and substrates (i.e., peat and sand). Red knots generally used sites with more prey. Miscellaneous prey (x[MACRON] = 2401.00/m.sup.2, SE = 169.16) influenced red knot presence at a site early in migration, when we only sampled on peat banks. Coquina clams (x[MACRON] = 1383.54/m.sup.2, SE = 125.32) and blue mussels (x[MACRON] = 777.91/m.sup.2, SE = 259.31) affected red knot presence at a site during peak migration, when we sampled both substrates. Few relationships between prey and red knot flock size existed, suggesting that other unmeasured factors determined red knot numbers at occupied sites. Tide and mean daily water temperature affected prey abundance. Maximizing the diversity, availability, and abundance of prey for red knots on barrier islands requires management that encourages the presence of both sand and peat bank intertidal habitats.

There is suboptimal early initiation of breastfeeding (EIBF) with widespread prelacteal feeding in Ghana. However, studies exploring the determinants of EIBF and prelacteal feeding are limited in Ghana. The study was conducted to assess the prevalence and determinants of EIBF and prelacteal feeding in Northern Ghana. This cross-sectional study was conducted among 508 mothers with infants aged 0-24 months in the Sagnarigu Municipality of Northern Ghana. The quantitative data were collected using a structured questionnaire adapted from Ghana's demographic and health survey. Multivariate logistic regression was used to identify the independent determinants of EIBF and prelacteal feeding. The prevalence of EIBF and prelacteal feeding was 72% and 21%, respectively. The independent positive determinants of EIBF were partner support to breastfeed [adjusted Odds ratio (AOR): 1.86, 95% Confidence interval (CI): 1.09-3.17] and exposure to breastfeeding information during pregnancy (AOR = 1.63 (95% CI: 1.01-2.64). Lower odds of EIBF were observed among mothers from extended family (AOR = 0.62, 95% CI: 0.41-0.95). Regarding prelacteal feeding, negative determinants were having a normal weight baby (AOR: 0.50, 95% CI: 0.27-0.90), exposure to breastfeeding information during pregnancy (AOR: 0.54, 95% CI: 0.31-0.92), while experiencing delayed onset of lactation was a risk factor for prelacteal feeding practice (AOR: 2.35, 95% CI: 1.41-3.94). In this study, EIBF was slightly higher than the 2030 global target on EIBF with widespread prelacteal feeding practice. Health programs aimed at improving EIBF should focus on the women partners, nutrition counselling, and support to mothers from the extended family. In the same vein, programs aimed at discouraging prelacteal feeding practice should also target women at risk, such as those with low birthweight babies and women experiencing delayed lactation onset.