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The study explored how information institutions can support the resilience of parents of adolescents affected by the Russia–Ukraine war. Ukrainian parents are facing major challenges of supporting their teenagers through a difficult developmental phase while also “buffering” their war-related hardships. By supporting parents, information institutions can also support children.

Fifteen parents were interviewed about mental health challenges and resources that are helpful and/or missing from their support systems. Recordings of participant narratives were analyzed using the qualitative thematic analysis technique.

The findings indicate that both teens and parents rely on internal resilience skills, family, friends and community resources to support themselves. However, a number of additional resources could be offered by information institutions, including content for (1) teens on developing skills in communication, interpersonal relationships, problem solving and academic pursuits; (2) parents on child development and opportunities in their host countries; both groups on (3) both groups on mental health first aid and safe spaces to meet peers. The study recommendations will be of interest to information professionals working with families, especially families affected by disasters.

The study relied on a small convenience sample of participants.

Study recommendations would be of interest to information professionals who develop and provide services to families affected by natural and manmade disasters.

Study recommendations improve understanding of the (potential) role of information institutions and libraries in strengthening family and community resilience.

The study offers a rare insight into experiences of war-affect families and provides evidence-driven recommendations for information institutions to support family and community resilience.

Due to the increasing frequency of extreme weather and densifying urban landscapes, residences are susceptible to heat-related discomfort, especially those in a naturally ventilated built environment in tropical climates. Indoor thermal comfort is thus paramount to building sustainability and improving occupants' health and well-being. However, to assess indoor thermal comfort considering the urban context, it is conventional to use questionnaire surveys and monitoring units, which are both case-centric and time-intensive. This study presents a dynamic computational thermal comfort modeling framework that can determine indoor thermal comfort at an urban scale to bridge this gap.

The framework culminates in developing a deep learning model for predicting the accurate hourly indoor temperature of urban building stock by the coupling urban scale capabilities of environment modeling with single-building dynamic thermal simulations.

Using the framework, a surrogate model is created and verified for Dharavi, India's informal urban settlement. The results indicated that the developed surrogate model could predict the building's indoor temperature in several complex new urban scenarios with different building orientations, layouts, building-to-building distances and surrounding building heights, using five different random urban representative scenarios as the training set. The prediction accuracy was reliable, as evidenced by the mean bias error (MBE) and coefficient of (CV) root mean squared error (MSE) falling between 0 and 5%. The findings also showed that if the urban context is ignored, estimates of annual discomfort hours may be inaccurate by as much as 70%.

The developed computational framework could help regulators and policymakers engage in more informed and quantitative decision-making and direct efforts to enhance the thermal comfort of low-income dwellings and informal settlements.

Up to this point, majority of literature that has been presented has concentrated on building a body of knowledge about urban-based modeling from an energy management standpoint. In contrast, this study suggests a dynamic computational thermal comfort modeling framework that takes into account the urban context of the neighborhood while examining the indoor thermal comfort of the residential building stock.