Good Corporate Governance (GCG) refers to the practices and processes that guide a company's operations and decision-making, significantly influencing its financial performance. This study employs secondary and quantitative data, utilizing the Systematic Literature Review (SLR) method, with sources obtained from the Google Scholar website. The research focuses on the impact of the Independent Board of Commissioners, the Audit Committee, and Managerial Ownership on financial performance. The findings indicate that effective corporate governance, particularly the presence of an independent Board of Commissioners, positively influences financial performance as assessed by Return on Assets (ROA). Additionally, the Audit Committee is shown to have a significant and positive effect on financial performance. In contrast, while Managerial Ownership does not appear to impact financial performance when evaluated through ROA, it does exhibit a positive correlation when assessed using Tobin's Q. This suggests that higher managerial ownership can enhance market perceptions of the company's long-term value and stability. The study concludes that the successful implementation of Good Corporate Governance practices can lead to improved financial performance for companies. Conversely, inadequate execution of these governance principles may result in diminished financial performance and overall company value. Therefore, it is crucial for organizations to prioritize and effectively implement GCG to foster better financial outcomes and enhance their market standing. This research underscores the importance of governance structures in shaping financial results and highlights the need for companies to focus on governance practices to achieve sustainable growth and value creation. Ultimately, the study emphasizes that a strong commitment to GCG can lead to increased investor confidence and long-term success in the competitive business landscape.

This systematic literature review explores the role of psychoneuroimmunology (PNI)-based mind-body interventions in improving wound healing within nursing practice. Wound healing is a complex, multifactorial process influenced not only by cellular and molecular factors but also by psychological and immune responses. Despite the growing body of evidence supporting the efficacy of mind-body practices such as guided imagery, hypnotherapy, and meditation in managing stress and modulating immune responses, there is limited integration of these interventions into clinical nursing practices. This review aims to bridge this gap by synthesizing studies published between 2020 and 2024 that examine the impact of these interventions on wound healing outcomes. The review follows the PRISMA protocol, analyzing data from 50 primary studies focusing on RCTs, systematic reviews, and quasi-experimental designs. The results show significant improvements in wound closure rates, pain reduction, and immune modulation (e.g., reduction in cortisol and pro-inflammatory cytokines) in patients who received mind-body interventions. The findings support the hypothesis that mind-body interventions, by addressing both psychological stress and immune function, enhance wound healing. The proposed framework for integrating PNI-based interventions into nursing practice could improve patient outcomes in chronic wound management. Future research should focus on long-term studies with larger sample sizes and standardized intervention protocols to further validate these findings.

Hospital Information Systems (HIS), or Sistem Informasi Rumah Sakit (SIRS), play a critical role in enhancing administrative efficiency, decision support, and healthcare service quality. However, their implementation and effectiveness vary significantly across healthcare settings, particularly in low- and middle-income countries (LMICs). This study aims to systematically evaluate the existing literature on HIS effectiveness, implementation barriers, and administrative impact. Using a PRISMA-based Systematic Literature Review (SLR) approach, we examined 14 high-quality studies from multiple scholarly databases including PubMed, Scopus, ScienceDirect, and Garuda. The review applied a hybrid thematic synthesis grounded in HOT-FIT and DeLone & McLean models, combined with a normalized quality scoring system. The findings reveal that HIS implementations positively influence administrative workflow, billing accuracy, and patient throughput, though outcomes are context-dependent. Key challenges include lack of interoperability, resistance to change, and insufficient training. Notably, regulatory mandates and national digital health policies were found to significantly enhance HIS adoption and sustainability. This review contributes a multidimensional synthesis of HIS performance, highlighting the importance of human, organizational, and policy alignment. It offers an evidence-backed framework for HIS evaluation that bridges theory and practice. We conclude that integrated, context-sensitive HIS models are essential for advancing hospital management and public health systems, and recommend further empirical studies on long-term impact and cross-sector integration.

Disaster preparedness and response are critical components of community health resilience, yet the role of homecare nursing within this framework remains underexplored. This study aims to investigate and synthesize existing research on how homecare nurses contribute to disaster management, with a focus on preparedness and response phases. Employing a systematic literature review (SLR) approach based on the PRISMA 2020 methodology, 38 peer-reviewed articles from major databases were analyzed thematically. Key findings reveal that homecare nurses play significant roles in early warning communication, individualized evacuation planning, and continuity of care post-disaster, particularly for vulnerable populations such as the elderly and chronically ill. However, evidence also indicates substantial gaps in training, policy inclusion, and structural integration into national disaster response systems. Thematic synthesis identified three dominant themes: decentralized preparedness, home-based continuity care, and systemic underrecognition of homecare in emergency frameworks. The study concludes that homecare nursing is a strategic yet underutilized asset in disaster risk reduction and recovery efforts. Strengthening policy, competency frameworks, and formal inclusion of homecare in disaster governance are necessary to enhance community resilience globally.

This study aims to address the challenges and propose solutions for the Optimization of Blockchain-Based Cybersecurity Systems to Enhance Resilience Against Ransomware Attacks using a Systematic Literature Review (SLR) approach. Blockchain is increasingly recognized as a transformative technology in cybersecurity due to its decentralized structure, transparency, and robustness in securing data. Despite these advantages, its widespread adoption is hindered by several challenges, including scalability, interoperability, high energy consumption, and limited access to representative ransomware datasets. This research highlights that integrating blockchain with advanced technologies such as data analytics, machine learning, and Explainable AI (XAI) can significantly enhance its effectiveness in combating ransomware.The findings reveal that Graph Convolutional Neural Networks (GCN) enable real-time detection of ransomware patterns in network traffic with an accuracy of up to 95%. Furthermore, Layer-2 solutions like the Lightning Network and sharding effectively alleviate the load on main blockchains, thereby increasing transaction throughput. Efficient consensus mechanisms, including Proof of Stake (PoS) and Delegated Proof of Stake (DPoS), address energy consumption issues, making blockchain more adaptable to IoT and resource-constrained environments. These approaches have proven successful in enabling early detection, mitigation, and prevention of ransomware in IoT systems, cloud infrastructures, and smart grid networks. The implications of this study underscore the potential of blockchain as a critical component of proactive and adaptive cybersecurity systems. However, overcoming existing challenges requires further development of hybrid frameworks that integrate blockchain with data analytics and machine learning technologies. In addition, efforts should focus on standardizing global security protocols to enhance interoperability and creating robust, diverse ransomware datasets to support more accurate detection systems. Future research should also explore methods to minimize latency and improve blockchain efficiency in real-time cybersecurity applications.

This research is a Systematic Literature Review (SLR) aimed at analyzing the application of Artificial Intelligence (AI) technology in the management of information technology (IT) projects. This study focuses on identifying the AI technologies employed, the benefits gained, and the challenges faced in implementing these technologies. The study gathers and analyzes literature from various leading databases, including Scopus, IEEE Xplore, and SpringerLink, within the timeframe of 2015–2025. The findings reveal that AI technologies such as machine learning, predictive analytics, and natural language processing play a significant role in improving efficiency, reducing risks, and supporting decision-making in IT project management. However, challenges such as data quality, organizational resistance, and implementation costs remain major obstacles in adopting this technology. This review provides comprehensive insights into trends, benefits, and barriers associated with AI utilization, along with recommendations for more effective implementation in the future.

Computer vision-based algorithms, especially Convolutional Neural Networks (CNN) and You Only Look Once (YOLO), have become the leading approaches in plant disease detection. CNN excels in extracting complex visual features for disease classification, while YOLO provides high-efficiency real-time object detection capabilities. Both algorithms have shown promising results in various studies, especially with controlled datasets. However, challenges remain in their application in real-world conditions, such as environmental diversity, overlapping symptoms, and poorly annotated data. Future research has the potential to optimize these algorithms through the development of lighter models, the use of transfer learning techniques, and multi-modal data integration. In addition, further exploration of a wider range of diseases, crops, and environmental conditions can expand the application of these algorithms. By leveraging these innovations, computer vision-based plant disease management can be improved to support sustainable precision agriculture.

This study aims to explore the impact of supply chain automation on workers’ well-being in the sustainable industrial sector through a Systematic Literature Review (SLR) approach. The transformation driven by Industry 4.0 technologies such as artificial intelligence (AI), Internet of Things (IoT), and blockchain, has presented significant challenges and opportunities. This study identifies the impacts of automation on workers’ economic, social, and psychological aspects, including changes in skills requirements, reduction of manual employment, and its effects on mental health. In addition, this study also highlights the importance of sustainability in automation implementation, by balancing economic efficiency, environmental preservation, and social welfare. By integrating the results of studies from various literatures, this study provides recommendations for policies that support sustainability and social inclusiveness, as well as strategic guidance for industries in adopting supply chain automation sustainably.

Optimal growth of broiler chickens and effective health monitoring are the main challenges in the livestock industry. Deep learning technologies, such as Differential Recurrent Neural Networks (DRNN), Long Short-Term Memory (LSTM), and Improved Feature Fusion Single Shot MultiBox Detector (IFSSD), offer innovative solutions for predicting chicken growth and detecting health conditions in real-time. This research aims to review the application of deep learning in growth forecasting and health monitoring of broiler chickens through systematic analysis using the PICO and PRISMA methods. Of the 150 studies identified, 30 met inclusion criteria for further analysis. The results show that DRNN and LSTM achieve high accuracy in processing temporal data, with an average growth prediction error of 1.8%. IFSSD excels in detecting chicken health via digital images, achieving 99.7% accuracy. Although the results are promising, challenges such as the need for high computing resources and the quality of data required for model training are major obstacles. This research suggests the need for investment in technological infrastructure, farmer training, and development of more efficient models to support the application of deep learning in broiler chicken farming. This technology has great potential to significantly improve production efficiency and animal welfare.