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Responsiveness is one of the key components of good governance and one of the ultimate goals of health systems. The purpose of this study was to investigate the importance and level of health system responsiveness (HSR) from the perspective of people with disabilities in Iran.

This cross-sectional study was carried out using multi-stage sampling in Kurdistan province in 2020. Of 1,067 participants, 889 and 520 had used outpatient and inpatient services, respectively. HSR questionnaire developed by World Health Organization was completed.

The dimensions of prompt attention (97%) and social support (81%) were the most and the least important dimensions for the respondents, respectively. In general, 43.6% of the respondents reported a “poor” status for HSR.

Designing targeted interventions to increase the level of health system responsiveness, especially with a focus on dimensions that are important to respondents but have weak performance, such as communication, confidentiality and autonomy, is suggested based on the findings of this study.

This is the first study performed on HSR from the perspective of people with disabilities in Iran. The findings of this study could be of interest to health policy makers to understand and improve healthcare experiences for marginalized populations globally.

The appearance of social media in small and medium enterprise (SME) business operations seems to be increasing in recent years. SME owners have started to understand that digital marketing tools can benefit their businesses significantly. Hence, in this study, we explore further the relationship between organisations and customers, and how SMEs use social media as an opportunity to develop their enterprises. We report the results by relying on qualitative methods to explore the insights from a wider stakeholder perspective. The findings contribute to the existing literature in agreement with the latest theories that SMEs in Greece are aware of the hidden opportunities and try to apply branding with the combination of social media. This study explores further the role of electronic word of mouth (eWOM) in a business transition, customers' experience and competitive business advantage.

The fishing cat's unique hunting strategies, including ambush, detection, diving and trapping, inspired the development of a novel metaheuristic optimization algorithm named the Fishing Cat Optimizer (FCO). The purpose of this paper is to introduce FCO, offering a fresh perspective on metaheuristic optimization and demonstrating its potential for solving complex problems.

The FCO algorithm structures the optimization process into four distinct phases. Each phase incorporates a tailored search strategy to enrich the diversity of the search population and attain an optimal balance between extensive global exploration and focused local exploitation.

To assess the efficacy of the FCO algorithm, we conducted a comparative analysis with state-of-the-art algorithms, including COA, WOA, HHO, SMA, DO and ARO, using a test suite comprising 75 benchmark functions. The findings indicate that the FCO algorithm achieved optimal results on 88% of the test functions, whereas the SMA algorithm, which ranked second, excelled on only 21% of the functions. Furthermore, FCO secured an average ranking of 1.2 across the four benchmark sets of CEC2005, CEC2017, CEC2019 and CEC2022, demonstrating its superior convergence capability and robustness compared to other comparable algorithms.

Although the FCO algorithm performs excellently in solving single-objective optimization problems and constrained optimization problems, it also has some shortcomings and defects. First, the structure of the FCO algorithm is relatively complex and there are many parameters. The value of parameters has a certain impact on solving optimization problems. Second, the computational complexity of the FCO algorithm is relatively high. When solving high-dimensional optimization problems, it takes more time than algorithms such as GWO and WOA. Third, although the FCO algorithm performs excellently in solving multimodal functions, it rarely obtains the theoretical optimal solution when solving combinatorial optimization problems.

The FCO algorithm is applied to the solution process of five common engineering design optimization problems.

This paper innovatively proposes the FCO algorithm, which mimics the unique hunting mechanisms of fishing cats, including strategies such as lurking, perceiving, rapid diving and precise trapping. These mechanisms are abstracted into four closely connected iterative stages, corresponding to extensive and in-depth exploration, multi-dimensional fine detection, rapid and precise developmental search and localized refinement and contraction search. This enables efficient global optimization and local fine-tuning in complex environments, significantly enhancing the algorithm's adaptability and search efficiency.