This chapter supports UN SDGs 7, 11, and 13 by offering a structure for urban policy-makers to create sustainable and energy-efficient cities by means of combined urban design, creative policy development, and environmentally friendly transportation options.
Elsevier,
The Lancet Obstetrics, Gynaecology, & Women's Health, Volume 1, Issue 1, September 2025, Page e1
The inaugural Editorial of the journal through which we pledged our commitment to improving the lives of half the world's population by acting as their voice and serving as a platform for continued advocacy on their behalf. Much has been written about problems in women's health; now is the time for solutions. Many of the historic issues in women's health will not be resolved overnight. The Lancet Obstetrics, Gynaecology, & Women's Health will continue this legacy and integrate women's health with obstetrics and gynaecology as an indivisible alliance between communities and disciplines.
The article examines the complex systemic risks and vulnerabilities arising from the transition to renewable energy and climate change, emphasizing the need for integrated risk management and collaboration to enhance resilience for first responders and policymakers.
This perspective argues that current WASH resilience strategies in low- and middle-income countries are insufficient, emphasizing the need for a transformative approach that addresses broader socio-environmental impacts of climate change. It proposes a research agenda focused on identifying which WASH systems to transform, the timing, leadership roles, and methods for monitoring and evaluating these transformations to achieve sustainable outcomes.
This paper highlights the potential of engineered mineral carbonation to transform cement and concrete into effective carbon sinks by enhancing CO2 storage while maintaining material performance, emphasizing the use of alkaline industrial residues and carbonatable binders. It proposes a strategic roadmap integrating scientific innovation, regulation, and carbon accounting to promote climate-positive construction.
This study examined the link between extreme heat waves and age-adjusted cardio-kideny-metabolic mortality in US counties from 2010 to 2019
The article proposes a predictive control strategy for Plug-in Hybrid Electric Vehicles (PHEVs) that utilizes route information from digital map providers to enable electric driving within zero-emission zones. The study develops a supervisory control with Dynamic Programming in the cloud to calculate an optimal State of Charge (SOC) trajectory, which is then used to guide the rule-based controller in the vehicle.
Lifestyle factors, including physical activity, dietary patterns, sleep, stress response, social connections, and use of risky substances, are increasingly recognized as predictors of cardiovascular disease (CVD) and cardiovascular health, with significant overlap between the American Heart Association's Life's Essential 8 guidelines and the 6 pillars of Lifestyle Medicine (LM). Assessing and addressing lifestyle factors have a foundational role in the prevention and management of cardiovascular disease and improving cardiovascular health.
Objective: In several countries, including the United States and United Kingdom, screening tests are conducted on men 65 years of age or older who smoke to diagnose abdominal aortic aneurysm (AAA) before rupture. In women, however, the incidence of AAA is low, so screening tests are not cost effective and therefore not recommended. Early detection and treatment of AAA are important for women as they face a four times higher risk of rupture and worse surgery outcomes compared with men.
Incorporating human behavioral factors into travel demand analysis is increasingly critical in transportation planning. Perceived security is a key behavioral factor influencing urban travelers’ choices, particularly for active transportation modes like cycling and vulnerable road users like women. While prior studies have explored certain aspects of perceived security —mainly for pedestrians—the application of Machine Learning (ML) models to predict cyclists’ perceived security remains a relatively developing research area.
