Explore the vast range of titles available.

This research explores the causes behind increasing defaults among Small and Medium Enterprises (SMEs) in Kuwait by focusing on the impact of the crisis and the role of the Kuwait National Fund for SME Development. The central crisis examined in this study is the COVID-19 pandemic, which acted as a catalyst that intensified pre-existing financial and operational challenges within the SME sector. Other systemic vulnerabilities such as limited financial literacy, weak risk management, and policy gaps are also considered within this broader crisis context. By employing a mixed method approach, the study draws on 15 interviews with owners of defaulted SMEs and a questionnaire involving 64 defaulted SMEs. This dual method framework qualitative (thematic analysis) and quantitative (survey based statistics) provides both depth and generalizability in understanding default risks. Thematic analysis revealed that crises such as COVID-19 related operational delays, changes in customer behaviour, and increased financial obligations were the main contributing factors. The quantitative findings align with these themes, with 89% acknowledging that the pandemic affected their ability to repay loans. This research contributes to existing literature by empirically linking crisis induced pressures to SME loan defaults in a Gulf context, with Kuwait as the focus. Policy implications include strengthening the Kuwait National Fund’s role in flexible financial support, risk management programs, and enhancing SME access to alternative funding channels. The study proposes that the Kuwait National Fund for SME Development could implement sustainability policies and practices such as flexible financial support, capacity building, risk management, and facilitating connections with alternative funding sources to address these issues. These proposed strategies provide SMEs with more resources and skills to handle challenges, which may reduce default rates and contribute to a more resilient SME sector in Kuwait.

PurposeThe research identifies literature on Home-Based Businesses (HBBs) from 2000 to August 2023, focuses on their economic roles, challenges for entrepreneurs and success strategies, reflecting societal and technological changes. This guides future studies and highlights knowledge gaps.Design/methodology/approachA systematic literature review of published, peer-reviewed research between the years 2000 and 2023 is performed to examine how research on HBBs has changed over time, areas needing more study and how research has been done.FindingsA total of 58 articles were analyzed and categorized into five distinct themes. Key insights into the evolution, significance and multifaceted aspects of HBBs are presented, revealing the impact and role of these businesses in a modern economic context.Originality/valueThe synthesis of existing literature enhances our understanding of recent studies on HBBs, focusing on challenges, and identifies promising directions for future research.

Purpose: Social tourism entrepreneurship is an important topic as it can resolve several social issues and generate sustainable community development. This research aims to examine the impact of social tourism entrepreneurship on sustainable tourism. Design/methodology/approach: The research used a systematic literature review analysis on 20 publications. The PICO framework was adopted to identify the publications on specific criteria, mainly the relativity with the subject. Findings: The findings indicated that social tourism entrepreneurship can have a high value for the tourist industry and a positive outcome for society and its sustainability. Three gaps were identified that need to be taken into consideration for future research. Original/value of the paper: There is a need to examine the existing knowledge on social tourism entrepreneurship and to indicate how new knowledge can be created. The value of this research is that it can be used as grounds for future research.

Satellite images often have very narrow brightness value ranges, so it is necessary to enhance the contrast and brightness, maintain the quality of visual information, and preserve pertinent details in the images before conducting additional analysis. This is because improving the brightness and contrast of images is crucial to image processing and analysis as it makes it easier for people to identify and comprehend the images. The Incomplete Beta Function (IBF) is a popular transformation function for Image Contrast Enhancement (ICE). Nevertheless, IBF has modest efficiency in parameter selection, a small set of adjustable parameters for stretching regions with high or low gray levels, and image enhancement is almost ineffective with stretching at either end. Meta-heuristic algorithms have been utilized efficiently and effectively over the past few decades to solve complicated image processing problems. This paper presents an Augmented version of the Elk Herd Optimizer (AEHO) combined with other traditional ICE techniques to improve edge details, entropy, local contrast, and local brightness of low-contrast natural and satellite images. The AEHO method employs a multi-stage strategic procedure, where its mathematical model undergoes several enhancements before being applied to ICE to allow for further exploration and exploitation of its features. This method uses a pre-established fitness criterion for the purpose of optimizing a set of parameters to rework a well-known transformation function and an effective assessment technique as an objective standard for this purpose. In the proposed image enhancement model, contrast limited adaptive histogram equalization was first applied as a prior step to ameliorate the color intensity. Then, the optimal IBF's parameters for ICE were adaptively determined using AEHO. After that, bilateral gamma correction was used to improve the visual quality of images without sacrificing edge details or natural color quality. The proposed AEHO-based image enhancement model is tested on natural scenes, certain standard images, and publicly available satellite images. In addition to other five techniques built on based on pre-existing meta-heuristics, the performance of the proposed method was compared against other well-known state-of-the-art image enhancement algorithms. The objective evaluation of the enhancement algorithms was achieved utilizing a variety of full-reference, no-reference, and pertinent performance evaluation norms. The experimental findings illustrated that the proposed image enhancement method can successfully outperform several other algorithms that employed the same image enhancement model as AEHO in addition to other conventional image enhancement methods included for comparison. The results on ten natural and satellite color images showed that the presented method performs better than all other comparative methods in the corresponding evaluation criteria in terms of average peak signal-to-noise ratio, average universal quality index, average structural contrast-quality index, and average values of discrete entropy results, which are more than 32.30, 94.0%, 0.98.9%, and 7.4, respectively. In a nutshell, AEHO can be an efficient method that can be used to tackle several image processing problems.

Upon thorough investigation, it was very evident and clear that the manuscript titled:\"Detection of the  prevalent  biofilm-producing  bacteria in hearing  aids and silico stimulation of effective anti-biofilm agents\" had  several citations which were inaccurately represented, cited out of context, or included without sufficient relevance to the study's content and conclusions. These issues compromise the scientific integrity and reliability of the article. The authors were contacted by the editorial office to provide clarification regarding these citations but did not respond within the stipulated timeframe In light of this, and in accordance with our commitment to maintaining the highest ethical standards in publishing, the editorial has decided to retract the article to prevent any potential misunderstanding or misuse of the published work.

Satellite images often have very narrow brightness value ranges, so it is necessary to enhance the contrast and brightness, maintain the quality of visual information, and preserve pertinent details in the images before conducting additional analysis. This is because improving the brightness and contrast of images is crucial to image processing and analysis as it makes it easier for people to identify and comprehend the images. The Incomplete Beta Function (IBF) is a popular transformation function for Image Contrast Enhancement (ICE). Nevertheless, IBF has modest efficiency in parameter selection, a small set of adjustable parameters for stretching regions with high or low gray levels, and image enhancement is almost ineffective with stretching at either end. Meta-heuristic algorithms have been utilized efficiently and effectively over the past few decades to solve complicated image processing problems. This paper presents an Augmented version of the Elk Herd Optimizer (AEHO) combined with other traditional ICE techniques to improve edge details, entropy, local contrast, and local brightness of low-contrast natural and satellite images. The AEHO method employs a multi-stage strategic procedure, where its mathematical model undergoes several enhancements before being applied to ICE to allow for further exploration and exploitation of its features. This method uses a pre-established fitness criterion for the purpose of optimizing a set of parameters to rework a well-known transformation function and an effective assessment technique as an objective standard for this purpose. In the proposed image enhancement model, contrast limited adaptive histogram equalization was first applied as a prior step to ameliorate the color intensity. Then, the optimal IBF’s parameters for ICE were adaptively determined using AEHO. After that, bilateral gamma correction was used to improve the visual quality of images without sacrificing edge details or natural color quality. The proposed AEHO-based image enhancement model is tested on natural scenes, certain standard images, and publicly available satellite images. In addition to other five techniques built on based on pre-existing meta-heuristics, the performance of the proposed method was compared against other well-known state-of-the-art image enhancement algorithms. The objective evaluation of the enhancement algorithms was achieved utilizing a variety of full-reference, no-reference, and pertinent performance evaluation norms. The experimental findings illustrated that the proposed image enhancement method can successfully outperform several other algorithms that employed the same image enhancement model as AEHO in addition to other conventional image enhancement methods included for comparison. The results on ten natural and satellite color images showed that the presented method performs better than all other comparative methods in the corresponding evaluation criteria in terms of average peak signal-to-noise ratio, average universal quality index, average structural contrast-quality index, and average values of discrete entropy results, which are more than 32.30, 94.0%, 0.98.9%, and 7.4, respectively. In a nutshell, AEHO can be an efficient method that can be used to tackle several image processing problems.