Evidence before this study: Acute appendicitis is the most common general surgical emergency in children. Its diagnosis remains challenging and children presenting with acute right iliac fossa (RIF) pain may be admitted for clinical observation or undergo normal appendicectomy (removal of a histologically normal appendix). A search for external validation studies of risk prediction models for acute appendicitis in children was performed on MEDLINE and Web of Science on 12 January 2017 using the search terms ["appendicitis" OR "appendectomy" OR "appendicectomy"] AND ["score" OR "model" OR "nomogram" OR "scoring"]. Studies validating prediction models aimed at differentiating acute appendicitis from all other causes of RIF pain were included. No date restrictions were applied. Validation studies were most commonly performed for the Alvarado, Appendicitis Inflammatory Response Score (AIRS), and Paediatric Appendicitis Score (PAS) models. Most validation studies were based on retrospective, single centre, or small cohorts, and findings regarding model performance were inconsistent. There was no high quality evidence to guide selection of the optimum model and threshold cutoff for identification of low-risk children in the UK and Ireland. Added value of this study: Most children admitted to hospital with RIF pain do not undergo surgery. When children do undergo appendicectomy, removal of a normal appendix (normal appendicectomy) is common, occurring in around 1 in 6 children. The Shera score is able to identify a large low-risk group of children who present with acute RIF pain but do not have acute appendicitis (specificity 44%). This low-risk group has an overall 1 in 30 risk of acute appendicitis and a 1 in 270 risk of perforated appendicitis. The Shera score is unable to achieve a sufficiently high positive predictive value to select a high-risk group who should proceed directly to surgery. Current diagnostic performance of ultrasound is also too poor to select children for surgery. Implications of all the available evidence: Routine pre-operative risk scoring could inform shared decision making by doctors, children, and parents by supporting safe selection of lowrisk patients for ambulatory management, reducing unnecessary admissions and normal appendicectomy. Hospitals should ensure seven-day-a-week availability of ultrasound for medium and high-risk patients. Ultrasound should be performed by operators trained to assess for acute appendicitis in children. For children in whom diagnostic uncertainty remains following ultrasound, magnetic resonance imaging (MRI) or low-dose computed tomography (CT) are second-line investigations.

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Urban Resilience (UR) enables cities and communities to optimally withstand disruptions and recover to their pre-disruption state. There is an increasing number of interdisciplinary studies focusing on conceptual frameworks and/or tools seeking to enable more efficient decision-making processes that lead to higher levels of UR. This paper presents a systematic review of 68 Scopus-indexed journal papers published between 2011 and 2022 that focus on UR. The papers covered in this study fit three categories: literature reviews, conceptual models, and analytical models. The results of the review show that the major areas of discussion in UR publications include climate change, disaster risk assessment and management, Geographic Information Systems (GIS), urban and transportation infrastructure, decision making and disaster management, community and disaster resilience, and green infrastructure and sustainable development. The main research gaps identified include: a lack of a common resilience definition and multidisciplinary analysis, a need for a unified scalable and adoptable UR model, margin for an increased application of GIS-based multidimensional tools, stochastic analysis of virtual cities, and scenario simulations to support decision making processes. The systematic literature review undertaken in this paper suggests that these identified gaps can be addressed with the aid of asset and disaster risk management methods combined with GIS-based decision-making tools towards significantly improving UR.

In construction projects worldwide, Main Contractors (MC) have to choose Sub-Contractor (SC) with varying degrees of previous experience. This adds an extra layer of uncertainty to the project that may impact costs and time directly or indirectly. Adequately assessing and selecting amongst alternative SCs is a well-known risk management strategy to mitigate threats and realize opportunities. But this is often done empirically. This paper discusses a mean to identify and weight SC and MC characteristics in each project's context in a way that enables a systematic and optimized decision-making process when selecting SCs in construction projects. Multi-criteria decision analysis (MCDA) and M-MACBETH software is applied to an empirical case study from a construction company in Malaysia. A cabling project of a double electrified railway in Malaysia provides the context for the decision-making problem, criteria, audit data and information. A sensitivity and robustness analysis is included in the paper.

Transportation network, especially highways, is considered a national or international asset, and by proper maintenance system, public and private organizations can prioritize the budgeting of repair and reconstruction. The problem is to have a reliable and practical model creating a solid understanding of the pavement degradation condition by inexpensive measurable parameters for municipalities. This study focuses on the road pavement condition, particularly the statistical evaluation of the processes of degradation involved in various road sections. Quantitative statistical analysis of a sample taken from the Iowa Department of Transportation (DOT) in the United States provides a better understanding of the needs in pavement maintenance processes. In addition, it can identify the critical factors of pavement maintenance. Through a case study, it is shown that organizations can develop a solid based statistical decision-making model using basic and low-priced parameters. The model has two approaches, with and without pavement type (used by creating several dummy variables to include each pavement type as independent variables). This study will positively enhance the pavement degradation prediction through a statistical analysis model and a case study of the Department of Transportation (DOT) of Iowa, USA. These details include explanatory models, bivariate correlation, principal component analysis, hierarchical and non-hierarchical clustering, creating dummy variables, and developing multivariate regression models.