As complex man-made systems that are home to the majority of the world population, cities have always faced a wide range of risks such as earthquakes. As the backbone of urban systems, physical components, including buildings, transportation networks, communication networks, and open and green spaces, are also vulnerable to disasters. To enhance the capacity to deal with disaster risks, enhancing urban resilience has recently become an essential priority for cities. This study aims to develop and pilot test a framework to evaluate urban physical resilience based on resilience characteristics and associated physical indicators. Interpretive Structural Modelling (ISM) was used to determine the relationships between physical indicators, and Multi-Criteria Decision-Making methods were applied to determine the relative importance of the characteristics. The results showed that the ‘Robustness of Building’, ‘Building Density’, ‘Aspect Ratio’, and ‘Street Width’ are the most important among the twenty physical indicators considered in the proposed framework. Subsequently, the proposed framework was applied to one of the districts of Kerman, a major city located in the southwest, earthquake-prone part of Iran. Overall results indicate low levels of physical resilience. The findings of this study can provide urban planners and decision-makers with more transparent and practical insights into the physical resilience of cities. Results can also be used to design and implement policies and programs to improve the current conditions.

The physical structure of urban settlements has become increasingly vulnerable to hazards following the growing trends of natural hazards, including earthquakes. The concept of resilience has gained momentum to facilitate better planning and response to such hazards. This research seeks to develop a conceptual spatial framework considering different phases of disaster risk management to evaluate urban physical resilience. Twenty indicators that define urban structure are identified and included in an Interpretive Structural Modeling—Analytic Network Process (ISM-ANP) hybrid model for analysis. The model and the indicator weights are adjusted using statistical and optimization techniques. District 4 of Tehran has been selected as the study area, and the proposed evaluation framework is applied to several zones with different physical urban structures. According to the results, the most important indicators of urban structure are the Robustness of Buildings, Street Width, Building Density, and Aspect Ratio. Sensitivity analysis and scenario-making are performed to explore the desired state of urban physical resilience for each zone. The results of the case study indicate moderate levels of urban physical resilience. The study provides more clear and practical insights into the concept of resilience to help urban planners and decision-makers improve urban physical resilience.

Wildfire susceptibility mapping (WSM) plays a crucial role in identifying areas with heightened vulnerability to forest fires, allowing for proactive measures in fire prevention, management, and resource allocation, ultimately leading to more effective fire control and mitigation strategies. This paper describes our undertaking to develop and compare the performance of two knowledge-based models, namely the analytic hierarchy process (AHP) and the technique for order performance by similarity to ideal solution (TOPSIS), as well as two novel genetic algorithm (GA)-based ensemble data-driven models: boosting and random subspace. The objective was to map susceptibility to forest fires in the Northern Mazar District in Jordan. The ensemble models were constructed using four well-known classifiers: decision tree (DT), support vector machine (SVM), k-nearest neighbors (kNN), and naive Bayes (NB) algorithms. This study utilized seventy forest fire locations and twelve influential factors to build and evaluate the models. To identify the optimal features for constructing the data-driven models, a GA-based wrapper method and four machine learning models were applied. During the validation phase, the area under the receiver operating characteristic curve (AUROCC) values for the single SVM, single NB, single DT, single kNN, GA-based boosting, GA-based random subspace, FR-AHP, and AHP-TOPSIS models were found to be 85.3%, 85.9%, 73.8%, 88.7%, 95.0%, 95.0%, 74.0%, and 65.4% respectively. The results indicated that the GA-based ensemble models outperformed both the single machine learning models and the knowledge-based techniques in terms of performance. The developed models in this study can be effectively utilized in various management and decision-making processes aimed at mitigating forest fire risks and enhancing fire control strategies.

Background: Very many social care clients have dementia, but few social care workers receive dementia‐specific training. Objective: To systematically review policy and research on the effectiveness of social care workforce dementia training. Methods: We searched PubMed, Embase, Scopus, CINAHL and Cochrane library, from November 2015 to February 2024, including studies describing dementia training and support interventions for social care workers. We narratively synthesized the evidence and assessed risk of bias with the Mixed Methods Appraisal Tool. We reviewed English policies from January 2015 to April 2024 to identify social and policy contexts relevant to dementia training. We consulted home care and care home stakeholders regarding how findings could inform future policy. Results: We included 56 studies (50 in care homes and 6 in home care). There was good quality evidence that two care home–based dementia training interventions that engaged staff ‘champions’ to integrate practice‐based learning reduced agitation, neuropsychiatric symptoms and antipsychotic prescribing and improved life quality of residents with dementia. One study found this approach was cost‐effective. In home care, evidence was limited; group training was valued and improved staff sense of dementia care competence in one study. We identified 27 policies and related documents and consulted 17 stakeholders. Stakeholders supported mandatory dementia training but considered implementation very challenging in current economic contexts. Conclusions: We found strong evidence for dementia training in care homes but a lack of research in home care. Policy options identified to implement evidence require investment, which could deliver substantial savings across health and social care.