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Bioretention is a widely used countermeasure to address stormwater runoff issues and restore the urban water balance. This review investigated the variety of designs and local contexts covered by earlier studies, as well as the means for assessing the hydrological performance of a bioretention system. It built on the analysis of 75 documents to discuss the adequacy of experimental setups or models for the evaluation of different performance indicators, and to summarise current knowledge regarding the impact of local context or design parameters on the hydrologic functioning of bioretention systems. The current literature was found to only partially cover the potential variety of local contexts or bioretention designs, and to sometimes omit critical information. Studies were for instance concentrated in regions with low seasonal rainfall variability for which limits the potential for investigating drought resilience issues and more generally restricts the applicability of their findings to other climate conditions. Regarding bioretention design, the use of environmental-friendly materials (renewable and local materials) as alternatives to traditional materials (sand, gravel, geotextile), as well as simpler designs with limited inputs of external materials (e.g. limited use of concrete or polymeric materials), remains largely overlooked. Besides, the over representation of lined system in current studies leads to a lack of understanding of the interactions between bioretention and the surrounding soil, despite evidence of their potential impact on the overall performance of bioretention in the case of unlined systems. In the reviewed studies, certain limitations of the most commonly used monitoring and modelling methods were identified. Event-based and short-term approaches made up a large proportion of the modelling and monitoring methods, but they lead to inaccuracies in annual and long-term performance. 1D models gained popularity due to their ease of use, but the simplification of configuration and hydrological processes and thus their influence on performance was rarely discussed., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2025 The Authors. Published by Elsevier B.V. All rights reserved.)

Extracting global and local feature information is still challenging due to the problems of retinal blood vessel medical images like fuzzy edge features, noise, difficulty in distinguishing between lesion regions and background information, and loss of low-level feature information, which leads to insufficient extraction of feature information. To better solve these problems and fully extract the global and local feature information of the image, we propose a novel transscale cascade layered transformer network for enhanced retinal blood vessel segmentation, which consists of an encoder and a decoder and is connected between the encoder and decoder by a transscale transformer cascade module. Among them, the encoder consists of a local-global transscale transformer module, a multi-head layered transscale adaptive embedding module, and a local context(LCNet) module. The transscale transformer cascade module learns local and global feature information from the first three layers of the encoder, and multi-scale dependent features, fuses the hierarchical feature information from the skip connection block and the channel-token interaction fusion block, respectively, and inputs it to the decoder. The decoder includes a decoding module for the local context network and a transscale position transformer module to input the local and global feature information extracted from the encoder with retained key position information into the decoding module and the position embedding transformer module for recovery and output of the prediction results that are consistent with the input feature information. In addition, we propose an improved cross-entropy loss function based on the difference between the deterministic observation samples and the prediction results with the deviation distance, which is validated on the DRIVE and STARE datasets combined with the proposed network model based on the dual transformer structure in this paper, and the segmentation accuracies are 97.26% and 97.87%, respectively. Compared with other state-of-the-art networks, the results show that the proposed network model has a significant competitive advantage in improving the segmentation performance of retinal blood vessel images., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Capacity development for disaster risk reduction is considered an important approach emphasized in the Sendai Framework for Disaster Risk Reduction 2015–2030. However, questions regarding progress and an effective approach remain. The purpose of this paper is, through a study in Sri Lanka, to identify the capacity development for disaster risk reduction by analyzing related strategies and methodologies for the development to occur. A capacity development project was taken as a case study in which 6709 people in disaster-affected areas in the southern, northern, and eastern areas of Sri Lanka were targeted. The primary data analysis used the results of the questionnaire survey comprising quantitative and qualitative questions to assess the training. The overall results indicate a positive response to the training, as the training themes matched the local people's needs and concerns. The indicators of overall evaluation and understanding knowledge scored higher compared to the relevance/application of the training scores. The psychosocial trainings were positively scored in all districts except one area, where war-affected people need special care. In addition, the participants valued the first aid training because of the high practicality thereof in their daily lives, and mixing soft and hard components in the training. The greater flexibility and adaptability are essential in the process of implementation.

This research investigated how socially sensed data can be used to detect ZIP level characteristics that are associated with spatial and temporal patterns of Emergency Department patients with a chief complaint and/or diagnosis of overdose or drug-related health problems for four hospitals in Baltimore and Anne Arundel County, MD during 2016-2018. Dynamic characteristics were identified using socially-sensed data (i.e., geo-tagged Twitter data) at ZIP code level over varying temporal resolutions. Data about three place-based variables including comments and concerns about crime, drug use, and negative or depressed sentiments, were extracted from tweets, along with data from four socio-environmental variables from the American Community Survey were collected to explore socio-environmental characteristics during the same period. Our study showed a statistically significant increase in adjusted rates of Emergency Department (ED) visits occurred between June and November 2017 for patients residing in ZIP codes in western Baltimore and northeastern Anne Arundel County. During this period, the three topics extracted from Twitter data were highly correlated with the ZIP codes where the patients were residing. Exploring the dynamic spatial associations between socio-environmental variables and ED visits for acute overdose assists local health officials in optimizing interventions for vulnerable locations., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Geographical variations in fertility have been observed within several countries in Northern Europe, with higher fertility in rural areas, smaller settlements and city suburbs. However, the processes underlying such fertility variations across residential contexts are not well understood. This paper contributes to the on-going debate by looking at local variations in fertility in Britain. It aims to disentangle the relative contribution of a number of factors, including the socio-economic characteristics of individuals, housing conditions, patterns of residential relocation and lastly, contextual factors stricto sensu. In addition, it seeks to identify those aspects of reproductive behaviour which are more likely to be associated with the observed spatial differences, and to distinguish between those that may be influenced by local context and those that respond to social influences at different scales. The focus is on local fertility contexts which, we argue, have the potential to influence the fertility behaviour of individuals through processes of social learning. Individual level data from the British Household Panel Survey and methods of event history analysis are used to explore women's transitions to second and third order births in Britain in the early 21st century. Our findings indicate that individual reproductive life paths respond to a variety of social processes acting at various scales, and that these influences vary by birth order. Most interestingly, local fertility contexts influence transition to first birth but not transition to higher order births, which are mainly associated with individual characteristics of women and their partners. Dominant spacing effects, however, suggest that local contexts have an indirect impact on second and third births through age at the onset of childbearing. The study demonstrates the importance of considering social interaction theories, and their extension to scale-sensitive spatial contexts in which these interactions take place, when analysing geographical variations in fertility. Future research seeking to explain subnational fertility variations must recognize the importance of developing theoretical understandings to inform empirical work., (Copyright © 2013 The Authors. Published by Elsevier Ltd.. All rights reserved.)