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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.

Heritage language loss among immigrant and diaspora communities represents a critical challenge to cultural identity, intergenerational communication, and cognitive diversity. Traditional heritage language maintenance approaches face significant barriers including limited resources, lack of qualified instructors, geographic dispersion, and competing demands of dominant language acquisition. This study investigates the effectiveness of bilingual digital story applications as innovative tools for heritage language maintenance among children aged 4-12 years. Through a 24-month longitudinal mixed-methods study involving 1,843 families across eight language communities (Spanish, Mandarin, Arabic, Korean, Tagalog, Vietnamese, Hindi, and Polish), we examined language proficiency development, cultural identity formation, family engagement patterns, and app usage behaviors. Quantitative analysis of pre- and post-intervention language assessments revealed statistically significant improvements in heritage language vocabulary (effect size d=0.68), listening comprehension (d=0.54), and oral production (d=0.47) among children using bilingual story apps for at least 20 minutes daily. Qualitative findings from parent interviews and child focus groups highlighted the apps' role in making heritage language learning enjoyable, facilitating parent-child interaction, connecting children to cultural narratives, and normalizing bilingualism. However, effectiveness varied substantially based on app design features, with interactive elements, culturally authentic content, parent involvement scaffolds, and adaptive difficulty showing strongest associations with outcomes. This research demonstrates that thoughtfully designed bilingual story apps can serve as accessible, scalable tools for heritage language maintenance, though they function most effectively as complements to rather than substitutes for rich home language environments and community connections. The study contributes empirical evidence to inform app development, family language planning, and policies supporting linguistic diversity in increasingly globalized societies.

: The rapid expansion of cybersecurity standards and threat intelligence frameworks has led to significant semantic fragmentation among security terminologies, hindering effective information retrieval and interoperability across systems. Traditional keyword-based search approaches are inadequate for capturing the contextual meaning of security terms, particularly within formal frameworks such as NIST, MITRE ATT&CK, and CWE. This study addresses this challenge by proposing CyberBERT, a transformer-based semantic search framework designed to align cybersecurity terminologies through deep contextual representation and ontology-driven reasoning. Research Objectives: The primary objective of this research is to develop a semantic retrieval model capable of understanding conceptual relationships between security terms beyond lexical similarity. Methodology: The proposed methodology fine-tunes a BERT-based model on the NIST Glossary corpus using a combination of masked language modeling and triplet loss objectives to generate discriminative semantic embeddings. These embeddings are further aligned with cybersecurity ontologies, including MITRE ATT&CK and CWE, to enhance semantic consistency and explainability. Semantic retrieval is performed using cosine similarity within a 768-dimensional embedding space and evaluated using Mean Reciprocal Rank (MRR) and Precision@K metrics. Results: Experimental results demonstrate that CyberBERT achieves an MRR of 0.832, outperforming domain-adapted baselines such as SecureBERT and CyBERT. The integration of ontology alignment improves semantic accuracy by over 6%, while robustness evaluations confirm resilience against adversarial linguistic perturbations. Visualization using t-SNE reveals coherent semantic clustering aligned with the five core NIST Cybersecurity Framework functions. Conclusions: In conclusion, CyberBERT effectively bridges semantic gaps across cybersecurity terminologies by combining transformer-based contextual learning with ontological reasoning. The framework offers a robust, interpretable, and scalable solution for semantic search, supporting improved interoperability and knowledge discovery in cybersecurity operations and standards harmonization.

Children living in orphanages face various psychological pressures, such as the loss of attachment figures, unstable social dynamics, and difficulty expressing emotions adaptively. These conditions require appropriate coping strategies to build emotional resilience. This community service activity utilizes a Service Learning (SL) approach, which integrates academic learning with community service. It aims to provide psychoeducation on emotions and coping strategies, and implements expressive writing as a means of emotional processing. The activity methods included interactive lectures, a pretest and posttest to measure understanding, an expressive writing therapy session, and a reflective interview at the end of the activity. Results showed an increase in understanding of emotions and coping strategies, as evidenced by improved posttest scores. Furthermore, expressive writing helped children express previously suppressed emotions, reduced psychological tension, and fostered a sense of relief, calm, and insight into personal problems. Overall, expressive writing has proven effective as a coping strategy in building emotional resilience in orphanage children, and the Service Learning approach has the potential for sustainable application in psychosocial support programs.

Daily drinking water requirement for healthy individuals is a crucial factor in maintaining health and physiological balance. For individuals involved in intense physical activity or exercise, fluid requirements can increase significantly, with recommendations reaching 3 to 4 liters per day to replace fluid loss through sweat, so researchers consider it important to discuss the provision of clean drinking water installations with the application of Reverse Osmosis (RO) machines that utilize PDAM water as a source, which is in the Candrasa complex to support the fulfillment of drinking water needs for the Indonesian Navy Academy Cadets. AAL is a military educational institution that requires a supply of safe and high-quality drinking water for the Cadets so that the learning and training process can run smoothly. This study covers the daily drinking water needs of AAL Cadets, the current condition of the drinking water supply, and the concept of the RO machine itself in the Candrasa complex by utilizing PDAM water to produce healthy and suitable drinking water for AAL Cadets. The results of this study are expected to increase the availability of clean and safe drinking water for AAL cadets, facilitate drinking water distribution in the Candrasa complex, support their quality of life during their education, training, and foster care, and help maintain their health and safety. This study also underscores the importance of efficient and sustainable water management in the military environment.

Implementation Artificial Intelligence as a supporting model for e-learning still faces several challenges. This is due to several aspects, such as concerns about loss spiritual touch, low digital literacy educators, limited technological infrastructure in educational institutions, and so on. On the other hand, the interaction that occurs between students and teachers is an emotional and moral dimension that occurs. The purpose study is to analyze the use AI as supporting model for e-learning in Islamic education. In this study, the method used is descriptive qualitative research. The findings study are (1) Views Islamic education practitioners on Artificial Intelligence, one which is that the existence Artificial Intelligence is not absolute replacement between students and teachers. However, Artificial Intelligence is considered means or tool in understanding learning; (2) The results  analysis explain that several AI (Artificial Intelligence) in e-learning in Islamic education have been present in several forms, including increasing access and inclusivity, translating and understanding classical manuscripts, analyzing learning and automatic assessment, material recommendation systems and learning personalization, as well Chatbots and virtual learning assistants; (3) Concept Islamic education is not only oriented towards the transfer knowledge (ta'lim), but also the formation of morals and spirituality (tarbiyah and ta'dib).

Relational bullying is a form of bullying that often occurs in the school environment and has a significant impact on the psychological and academic aspects of students. This research aims to analyze the influence of relational bullying on students’ interest in learning at various levels of education. The research method used is literature review by reviewing 11 relevant national journals indexed by Google Scoler, DOAJ and Sinta, which were published between 2018 and 2024. The results of the study show that relational bullying generally appears in the form of exclusion, spreading gossip, manipulation of social relationships, indirect mockery, and seniority behavior. The impacts caused include a decrease in academic confidence, increased anxiety and depression, loss of study concentration, and low student motivation and participation in class. This condition implies a decrease in learning achievement and student involvement in school activities. Based on the findings, it is concluded that relational bullying has a significant negative effect on students’ interest in learning. Therefore, prevention efforts are needed through the establishment of a positive school climate, strengthening the value of empathy, and the provision of sustainable counseling services to create a safe learning environment and support the development of students.

This community service activity aims to improve the financial literacy of Micro, Small, and Medium Enterprises (MSMEs) through targeted training in calculating the Break Even Point (BEP) as a financial control tool. BEP, or break-even point, is the specific point at which total revenue equals total costs, allowing businesses to determine the minimum sales volume required to avoid losses. The activity was held in the Curug Tourism Village in Bogor Regency, with 27 MSMEs from various business sectors actively participating. The implementation method included outreach, training sessions, practical BEP calculations using participants' actual business data, and post-training mentoring to ensure long-term learning. The results of the activity demonstrated a significant improvement in participants' understanding of the BEP concept and its practical application in determining selling prices, production volumes, and cost control. Therefore, the application of BEP analysis is a crucial and effective strategy in supporting the sustainability and operational efficiency of MSME businesses.

Myalgia is a muscle pain condition that can occur in a specific area of the body or spread to several regions. Its main symptoms include pain, soreness, or discomfort in the muscles, whether during movement or at rest. The pain may range from mild to severe and is sometimes accompanied by stiffness, muscle tension, or even cramps that interfere with daily activities. This condition is commonly experienced by the elderly due to aging, loss of muscle mass, improper physical activity, stress, lack of stretching, or an unhealthy lifestyle. This community service activity aimed to provide education and raise awareness about myalgia among 10 elderly participants in the Elderly Community of Kalirejo, Malang, to help them better understand prevention, pain management, and self-care strategies. The method used was service learning (SL) with achievement parameters measured through pre-test and post-test evaluations. The results showed an increase in participants’ knowledge regarding the definition, causes, and rehabilitation of myalgia from 10% to 90%, demonstrating that this program effectively improved the elderly’s understanding and awareness of myalgia.

This study examines the revitalization of the Qur’anic Education Center (TPA) in Mekar Jaya Village, which had ceased operating due to the loss of its main teacher and limited volunteer support. Using a Participatory Action Research (PAR) approach, the program engaged community members, volunteers, religious leaders, and parents in collaborative efforts to restore regular Qur’an learning activities and strengthen the TPA’s function as a vital center of Islamic education. The intervention led to increased student participation, including the return of former learners and the enrollment of new participants, as well as improved learning motivation through active engagement and parental support. This collaborative revitalization not only revived essential Islamic learning for children but also fostered sustained community involvement, highlighting the critical role of volunteers and local leadership in sustaining nonformal religious education. The model developed through this initiative provides practical implications for other rural communities seeking to maintain and strengthen Islamic educational institutions.

This study aims to determine the level of problematic smartphone use (PSU) and revenge sleep procrastination (RBP) among MTs/SMP students in District X, while also examining the relationship between the two variables. The problem of excessive smartphone use among adolescents is increasingly important to study because it can impact sleep quality, learning concentration, and mental health. This research approach uses a quantitative correlational method with data collection through a bold questionnaire (Google Form). The research sample consisted of 103 MTs/SMP students in District X who were selected using an incidental sampling technique. The RBP variable measurement scale was developed by the researcher with reference to aspects proposed by Kroese et al. (2014). Meanwhile, the PSU Scale was developed based on dimensions formulated by Foerster et al. (2015), including withdrawal, desire, loss of control, dependence on peers, and negative life consequences. The results of the correlation analysis showed that PSU had a very strong positive relationship with RBP (r = 0.833) and was statistically significant (p < 0.000). This means that the higher a person's tendency to experience problematic smartphone use, the greater the likelihood of revenge bedtime delay. Among the dimensions of PSU, withdrawal showed the highest correlation with RBP (r = 0.831). This was followed by negative life consequences (r = 0.778), craving (r = 0.577), loss of control (r = 0.489), and dependence on peers (r = 0.333). Overall, this study concludes that PSU plays a significant role in increasing RBP trends among students. These findings highlight the urgent need for awareness and intervention programs to reduce excessive smartphone use among adolescents. Therefore, the results of this study are expected to serve as a reference for schools and parents in designing strategies to prevent the negative impacts of smartphone use on adolescents.

This study presents a machine learning approach for predicting the dimensions of microstrip antenna slots based on antenna performance parameters such as frequency, gain, directivity, return loss (S11), radiation efficiency, and VSWR. A two-phase methodology was employed. In the first phase, ten regression algorithms were evaluated, and Random Forest was identified as the most effective model based on Mean Absolute Error (MAE) and R-squared (R²) scores. In the second phase, hyperparameter tuning was conducted using Grid Search to further improve the model’s performance. The optimized Random Forest model demonstrated consistent improvements in predictive accuracy, with R² values increasing across all output variables. These results indicate that the combination of regression-based modeling and systematic hyperparameter tuning is effective for capturing complex relationships in antenna design tasks. The proposed approach offers a promising data-driven alternative for geometric prediction in microstrip antenna development, particularly when analytical models are insufficient.

The digital design of cultural museums with an interactive and educational approach in Indramayu Regency aims to present a modern, innovative, and interesting cultural preservation space. This concept was born from the urgent need for a means that are able to preserve, document, and promote local cultural wealth in the midst of the rapid flow of globalization. The lack of cultural literacy among the public, especially the younger generation, as well as the loss of historical objects due to inadequate storage facilities, are the main reasons for the importance of developing digital-based museums. Through an interactive approach, visitors can actively engage with cultural collections using advanced technologies such as augmented reality (AR), virtual reality (VR), and interactive multimedia. This technology provides an immersive, engaging, and personalized learning experience, so that visitors not only see, but also virtually experience the richness of the culture on display. Meanwhile, the educational approach is designed to create an informative, collaborative, and fun learning environment, which is highly relevant for learners and the younger generation as the successors of cultural preservation. Indramayu Regency, known for its long history, maritime traditions, and distinctive coastal culture, has great potential to become a center for cultural education. This digital museum will utilize digital technology to document cultural narratives in depth, so that it can be accessed not only by local people, but also national and international tourists. With its strategic location in the city center, the presence of this museum is expected to encourage the participation of the public and tourists in understanding, appreciating, and preserving the local culture of Indramayu. Apart from being an educational facility, this museum is expected to be able to become a driver of local tourism that makes a positive contribution to the economy of the surrounding community. Digital cultural museums are not only a place for preservation, but also a symbol of a strong regional identity, reflecting the synergy between technological innovation and commitment to cultural sustainability in the modern era.

Pneumonia detection from chest X-ray images is widely used in computer-aided diagnostic systems. However, effective clinical decision support requires not only accurate classification performance but also consideration of unequal error costs, since false negative predictions may lead to more severe consequences than false positives. In addition, prediction probabilities must be well calibrated to support threshold-based medical decisions such as triage and patient escalation. This research investigates asymmetric misclassification costs and probability calibration for binary classification (PNEUMONIA vs. NORMAL) using the Hugging Face dataset hf-vision/chest-xray-pneumonia. The proposed framework utilizes a ResNet-18 architecture integrated with cost-sensitive learning through weighted cross-entropy loss (FN:FP = 5:1), threshold optimization based on validation data to reduce expected cost, and post-hoc temperature scaling for improving probability calibration. Experimental results on the independent test set indicate that the cost-sensitive approach enhances specificity and decreases expected cost compared to the conventional cross-entropy baseline. Furthermore, temperature scaling improves the reliability of probabilistic predictions, as demonstrated by better negative log-likelihood and Brier score values. The study also explores selective prediction strategies to balance prediction coverage and risk reduction, complemented by Grad-CAM visualizations and structured failure-case analysis for qualitative assessment. Overall, the findings demonstrate that incorporating cost-aware decision thresholds and calibrated probability estimates can serve as lightweight yet effective enhancements for chest X-ray classification systems in clinical decision-support applications.