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Corporate insolvency regimes have long been designed around efficiency, creditor recovery, and procedural certainty, frequently marginalizing the human, social, and distributive consequences of corporate failure. This qualitative literature review seeks to reconceptualize insolvency as a multidimensional institutional process by integrating the principles of humanity, resilience, and equity, with the objective of developing fairness metrics for more inclusive insolvency systems. Drawing on interdisciplinary scholarship from insolvency law, corporate governance, economic sociology, and normative political theory, this study systematically synthesizes peer reviewed literature published between 2000 and 2024 using a structured qualitative thematic analysis. The review identifies three interrelated dimensions shaping inclusive insolvency outcomes. First, humanity-oriented approaches emphasize stakeholder vulnerability, dignity preservation, and procedural justice, particularly for employees, involuntary creditors, small suppliers, and local communities affected by corporate collapse. Second, resilience based perspectives frame insolvency not merely as an endpoint of failure but as an adaptive governance mechanism that enables organizational recovery, institutional learning, and broader systemic stability. Third, equity focused frameworks highlight the importance of proportional and context sensitive loss allocation, stakeholder participation, and intertemporal fairness in distributing the economic and social costs of insolvency. By integrating these dimensions, the study develops a conceptual framework of fairness metrics that extends beyond traditional efficiency-driven indicators, offering normative and analytical tools for evaluating insolvency systems in a more holistic manner. The findings contribute to insolvency scholarship by bridging fragmented theoretical strands and advancing a human-centered and resilience oriented understanding of corporate failure. The review further suggests that insolvency regimes embedding humanity, resilience, and equity are more likely to enhance institutional legitimacy, stakeholder trust, and long term economic sustainability, thereby providing a robust foundation for future empirical research and policy reform.

Various floods in Jayapura City in early 2025 have caused a number of losses. The problem of the impact of this flood can be solved by reducing the occurrence of flooding again, especially in the long term through the formation of children's character who care about the environment. Activities to increase environmental awareness were carried out by planting flowers focused on children studying at the Rumbel Pelita located at Kali Sunyi, Polda Bhayangkara Public Housing Complex, Buper Waena. The purpose of this community service activity was to increase children's awareness of the surrounding environment through flower planting. The problem posed was how to change children's behavior to care about the environment to prevent flooding. The solution was to increase children's awareness through flower planting at the Kali Sunyi location, Polda Bhayangkara, Buper Waena, Heram District. The method used was through counseling, question and answer sessions, and flower planting activities accompanied by their care. This care was carried out primarily during the learning process at the Rumbel that took place in the future after the community service activity. The results of this community service have been carried out by planting approximately 165 flowers at the Rumbel location, planted by 50 children.

Digital images often experience noise disturbances that can reduce visual quality and interfere with the image analysis process. One common type of noise is salt and pepper noise, especially in grayscale images, which is characterized by the random appearance of black and white dots. This study applied the Deep Convolutional Autoencoder (DCAE) method with a skip connection mechanism to eliminate salt and pepper noise in grayscale images measuring 256×256 pixels. The dataset used consists of 300 pairs of clean images and noisy images that have gone through the preprocessing stage, including normalization and data augmentation. The model was trained using an Adam optimizer with a Mean Squared Error (MSE) loss function and validated through a train-test split scheme to avoid overfitting. Model performance was evaluated using Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) metrics. The test results showed that the DCAE model with skip connections was able to effectively reduce noise while maintaining the main structure of the image based on the PSNR and SSIM values obtained, and showed better performance than conventional median filters. In addition, the model was successfully implemented into a Streamlit-based application to perform the image denoising process interactively, making it easier for users to experiment and visualize results in real-time.

Flood disasters that struck Cikahuripan Village significantly affected the physical, psychological, and learning motivation conditions of elementary school students, particularly at SD N 1 Cikahuripan. In the post-disaster period, students experienced a decline in learning motivation caused by psychological trauma, loss of a sense of security, and limited learning facilities. This Community Service Program (Pengabdian kepada Masyarakat/PkM) aimed to restore students’ psychological conditions while strengthening their learning motivation through an integrated psychosocial and pedagogical approach. The implementation methods included an initial assessment of students’ conditions, trauma healing activities based on play therapy, and the application of Fun Learning methods combined with the “Tree of Dreams” activity to rebuild students’ intrinsic motivation. The program was conducted from 15 to 22 November 2025 and involved 31 elementary school students as well as teachers as sustainability partners. The evaluation results showed significant positive changes, indicated by increased cheerfulness, active participation, confidence in social interaction, and improved learning focus among students. In addition, teachers’ capacity to implement trauma-sensitive teaching practices also improved. This program demonstrates that post-disaster learning motivation recovery requires a holistic approach integrating psychological and academic recovery, and it has the potential to serve as a replicable model for educational interventions in disaster-prone areas.

This research aims to develop a multiclass flower image classification system using the Convolutional Neural Network (CNN) algorithm with the EfficientNet architecture. The main problem addressed is the difficulty of manual identification of flower species that share high visual similarity. The research stages include collecting 17,299 flower images across 19 classes, performing data preprocessing such as image resizing, pixel normalization, and augmentation, followed by model training using the EfficientNet transfer learning approach. The model was trained for 10 epochs with an 80:20 training-validation data split. The evaluation results show that the model achieved a validation accuracy of 98.05% with a loss value of 0.0968, and an average precision, recall, and F1-score of 0.98. The trained model was then implemented into a web-based application built using the Next.js framework, enabling users to upload flower images and obtain real-time classification results via the Hugging Face API. The system successfully identified flower species with a confidence level of 99.87%. These findings demonstrate that combining a modern CNN architecture with transfer learning provides efficient and highly accurate flower classification performance, which can be effectively implemented for educational and digital conservation purposes.

Detection and identification of plant diseases is critical to the success and efficiency of agricultural production. Plant disease outbreaks are becoming more frequent throughout the world, and the presence of these diseases in cultivated plants has a significant impact on productivity. Therefore, researchers are focusing on developing effective and reliable plant disease detection methods. Thus, farmers can take advantage of early detection of this disease to minimize future losses. This article discusses machine learning approaches as well as decision trees, K-nearest neighbors, naive Bayes, support vector machines (SVM), and random forests for detecting coffee leaf diseases using leaf images. The above-mentioned classifications were researched and compared to determine the most suitable plant disease prediction model with the highest accuracy. Compared with other classification algorithms, the SVM algorithm achieves the highest accuracy of 99.75%. All the models trained above will be used by farmers to quickly identify and classify new diseases in images as a prevention strategy. As a preventive measure, farmers can detect and classify new diseases in images early.

This study aims to describe the forms of emotional trauma experienced by adolescent victims of verbal violence in Tuntungan Village 1. The study used a qualitative approach with a case study method on three adolescents who were purposively selected based on their experiences of verbal violence in their family environment. Data collection techniques included in-depth interviews, participant observation, and documentation to obtain a comprehensive picture of the psychological condition of the research subjects. The obtained data were then analyzed descriptively qualitatively through the processes of reduction, presentation, and drawing conclusions. The results of the study indicate that verbal violence has a significant impact on the emotional health of adolescents, including decreased learning motivation, the emergence of withdrawal behavior from social environments, and the loss of a sense of security and comfort within the family. In addition, harsh, demeaning, and repetitive family communication patterns form a negative self-concept in adolescents, which has the potential to affect personality development and social relationships in the future. The findings of this study emphasize the important role of families, schools, and communities in creating a supportive environment, as well as the need for preventive efforts and psychosocial interventions to prevent and address emotional trauma in adolescents on an ongoing basis.