BackgroundThe war in Tigray has caused humanitarian and public health crises in North Ethiopia in addition to a high prevalence of mental health symptoms among the Tegaru (people from Tigray) diaspora in Australia. To date, there have been no studies that have explored mechanisms of vicarious mental health and resilience among Tegaru diasporas in Australia.MethodsWe conducted 14 in-depth interviews with Tegaru people living in South Australia from September 2023 to June 2024. Data analysis involved an initial stage of open coding, followed by conceptual coding. We also applied "interanalyst validity" to validate the code of framework.ResultsAll participants talked at length about various aspects of vicarious trauma, including (a) psychological and emotional impacts such as media-induced stress, communication loss, anger, trauma related to family loss, and gender-based violence; (b) physical impacts, including sleep disturbances and health deterioration; (c) social impacts, such as narrative hate, perceived betrayal, and disruption of social ties; and (d) economic impacts, including financial strain and job loss. Coping mechanisms included strategies such as avoiding non-Tegaru media or community, religious support, social networking, and mental resilience.ConclusionsThe findings highlight the multifaceted and far-reaching impacts of the Tigray conflict on the Tegaru diaspora in Australia, extending beyond immediate psychological distress to broader social, economic, and communication aspects. The identified coping strategies underscored both the resilience and vulnerability of affected individuals. The findings imply the urgent need of culturally adapted peer-support and trauma-informed care programs for diaspora communities.
PurposeThis study advances the audit impact literature by developing guiding principles and a conceptual framework designed for measuring and reporting performance audit impact on operational improvement. The study integrates theoretical frameworks and international practice to address the gap in how audit agencies can measure and demonstrate their value in terms of operational outcomes.Design/methodology/approachThe study synthesises widely used theoretical frameworks and maps their application to seven public audit agencies. Using case comparisons, the study identifies alignments, gaps and emerging lessons. These insights are translated into guiding principles that underpin the proposed conceptual framework.FindingsCurrent practice exhibits strengths such as multi-perspective reporting and recommendation tracking, but also weaknesses including attribution difficulties, under-measurement of societal outcomes and reliance on readily available metrics. The proposed framework addresses these limitations by emphasising measuring what matters, supporting attribution, engaging stakeholders, linking findings to verified change, tailoring to context and transparent communication.Research limitations/implicationsThe analysis is restricted to Anglophone jurisdictions and relies on secondary self-reported information from the agencies.Practical implicationsThe study offers audit agencies a structured tool to diagnose gaps in their impact reporting. By selectively adopting framework components that fit their mandate, resources and audiences, agencies can articulate their value narrative more systematically and better communicate operational outcome improvements.Originality/valueWhereas prior studies have examined frameworks or agency practices in isolation, this research synthesises these to produce actionable operational guidance. The framework provides a structured basis for analysing and communicating the operational impact of performance audits.
In this paper, based on mathematical principles, we propose a new optimization algorithm, for solving large-scale numerical problems and complex real-world engineering tasks. This optimizer, named Bezier curve-based optimization (BCO), draws inspiration from Bezier curve theory. Drawing upon the geometric properties of different-order Bezier curves, BCO employs the linear Bezier curve to achieve exploitation, the quadratic Bezier curve to facilitate local optima avoidance, and the cubic Bezier curve to implement exploration. Furthermore, in the algorithm, we incorporate an adaptive exploitation-exploration balance factor that automatically maintains the balance between local exploitation and global exploration throughout the search process. First, we test BCO on 23 standard benchmark functions. Next, we evaluate its performance using the CEC2017 test suite with 10-, 30-, 50-, and 100-dimensional problems. Then, we further validate BCO’s effectiveness by combining test sets from CEC2014, CEC2020, and CEC2022 suites. Finally, we confirm BCO’s practicality by successfully applying it to 15 real-world engineering problems with constraints. The results of BCO are compared against those of 27 well-chosen algorithms, including 6 well-known, 8 cutting-edge, 8 top-performing hybrid, and 5 CEC champion algorithms. These comparisons show that BCO exhibits competitive capabilities in exploration and exploitation, balancing the two, convergence rate, avoiding local optima, and applicability. Finally, BCO is successfully applied to 3D unmanned aerial vehicle (UAV) path planning, which covers two types of waypoints, with each type corresponding to eight different terrain scenarios, and also takes into account the requirement of efficiently evading multiple threats. In comparison with 7 outstanding algorithms selected from the original 27, BCO achieves the top rank in the Friedman test, highlighting its superiority and competitiveness. These findings suggest that BCO is a powerful new tool for addressing complex real-world challenges, with promising potential for advancing future optimization research. This paper also presents a comprehensive collection of 312 metaheuristic algorithms, including multiple contributions from various research teams; by analyzing these algorithms, it uncovers non-uniform evolutionary patterns, offering readers a complete overview of the field. The source code of BCO is publicly available at https://ww2.mathworks.cn/matlabcentral/fileexchange/183097-bezier-curve-based-optimization-bco.
Starfish optimization algorithm (SFOA) is a bio-inspired metaheuristic algorithm for global optimization, which has demonstrated accuracy and efficiency in popular benchmark functions. However, for complex practical problems such as engineering design and feature selection, SFOA still requires a better balance between exploration and exploitation to ensure robust performance in real-world applications. In this paper, we present an improved SFOA algorithm named ORLGSFOA, which integrates opposition-based learning, reinforcement learning, and the growth optimizer with the basic SFOA. The algorithm first incorporates the opposition-based learning strategy during initialization to improve the diversity and quality of the initial solutions. Then, the updating rule from the growth optimizer is hybridized with SFOA to balance exploration and exploitation. Moreover, ORLGSFOA integrates the reinforcement learning strategy to reward the winner from SFOA and growth optimizer by adding updating positions during optimization to enhance global convergence. Experiments demonstrate the superior performance of ORLGSFOA. In comprehensive benchmark tests on 65 functions from classical, CEC2017, and CEC2022 suites, ORLGSFOA outperformed 15 other metaheuristic algorithms by achieving more accurate solutions. Additionally, this effectiveness translates directly to real-world applications, as is evidenced by tests on seven engineering design problems. Besides, the effectiveness of ORLGSFOA in solving discrete combinatorial optimization problems is verified through 52 feature selection problems, and the algorithm is extended to the wind engineering scenarios. In conclusion, ORLGSFOA demonstrates powerful efficacy in addressing a wide range of challenges, including global optimization, engineering design, and feature selection problems. The source code of ORLGSFOA is publicly available at: https://ww2.mathworks.cn/matlabcentral/fil eexchange/183223-orlgsfoa.
Global and constraint optimization in engineering structural design problems often involves more complex types, which increases computational complexity. To address this challenge, this paper constructs an exploration mechanism inspired by the hunting behaviours of marine octopuses, along with an exploitation mechanism based on their mating behaviours. These mechanisms aim to balance convergence speed and solution accuracy using a specially designed stochastic regulatory factor. This paper develops a nature-swarm phenomenon-based search strategy and mathematical model, named the octopus optimization algorithm (OOA), by simulating processes of octopuses searching for potential prey, escaping natural predators, attacking prey, and mating behaviours. In addition, inspired by the water-spraying recoil and transient acceleration phenomenon, a recoil motion-based stochastic feedback mechanism is proposed by designing a unique recoil operator to achieve information exchange in different search spaces. To demonstrate the universal applicability of the proposed OOA algorithm, we qualitatively analysed swarm convergence and swarm search behaviours, population diversity, exploration and exploitation performance on 84 benchmarks covering unimodal, multi-modal, fixed-dimensional, and composite functions and quantitatively verified convergence, effectiveness, significance, robustness, population diversity, exploration and exploitation efficiency, progressive scalability, and parameter sensitivity on the CEC2017 suites with 10, 30, 50, and 100 dimensions. Moreover, OOA beats 12 highly cited competitors in terms of computational performance when solving different optimization problems. Based on the pairwise comparisons-based Wilcoxon test and multiple pairwise comparisons-based Friedman test, it indicates that compared to 12 state-of-the-art algorithms, OOA achieved a mean rank of 1.19 across 84 benchmarks. The non-parametric test significance results show OOA contains 981 positive signs out of 1008 comparisons (84 benchmarks), with an optimization efficiency of 97.3%. On the CEC2017 suites, the mean ranks across four dimensions were 1.22 with 10Dim, 1.0 with 30Dim, 1.0 with 50Dim, and 1.0 with 100Dim, respectively, all ranking first. The non-parametric test results indicate OOA contains 1427 positive signs out of 1440 comparisons (120 benchmarks), with a solution efficiency of 99.1%. Thus, the proposed OOA algorithm demonstrates statistically significant advantages in computational performance and scalability. OOA has achieved better results than competitors in eight engineering problems, showing superior computational efficiency and reliability. The source code of OOA is publicly accessible at https://ww2.mathworks.cn/matlabcentral/fileexchange/183324-recoil-mechanism-based-octopus-optimization-algorithm-ooa and https://github.com/kaiguangnxu/OOA.
