مطالب

Despite UNCLOS serving as the primary legal framework governing maritime conduct, challenges remain in effectively embedding human rights principles, ethical governance and inclusivity within its implementation, especially in geopolitically sensitive regions such as Malay Indo Archipelago. This raises the question of how fundamental legal traditions, such as those found in 15 th century Undang-Undang Melaka, a customary maritime code emphasizing human dignity, ethical conducts, and inclusive community governance- can highly inform and enrich contemporary international maritime law. The problem lies in the apparent disconnect between modern state centered ethical governance regimes and the human centered ethical governance principles that underpin long lasting peace. This study critically examines how the human right norms embedded in the Undang- Undang Melaka align with or differ from those in UNCLOS, arguing that the incorporation of these ethical and inclusive governance principle is crucial for fostering maritime cooperation, trust, and ultimately geopolitical peace. By exploring these normative intersections through a qualitative legal analysis, the research aims to demonstrate that the revitalization of customary law’s humanistic maritime governance, promoting more equitable and peaceful relations among maritime states. 

Objective: This study aimed to determine the fit of a structural model of death anxiety and self-acceptance based on psychological flexibility and differentiation, with the mediating role of self-compassion in the elderly. Methods and Materials: The research method was fundamental, descriptive, and correlational in nature. The statistical population consisted of all individuals aged 60 years and above residing in daily elderly care centers in Nowshahr and Chalous counties from February 2024 to August 2024 (N = 232). Due to the limited size of the population, a census method was used, and research questionnaires were distributed to all members of the population. Ultimately, due to damaged or incomplete questionnaires, data from 215 questionnaires were analyzed (n = 215). The research instruments included the Templer Death Anxiety Scale (1970), the Unconditional Self-Acceptance Questionnaire by Chamberlain and Haaga (2001), the Acceptance and Action Questionnaire-II (Bond et al., 2007), the Differentiation of Self Inventory by Skowron and Friedlander (1998), and Neff’s Self-Compassion Scale (2003). Data analysis was conducted using SPSS version 26 and LISREL 8 software. Findings: Pearson correlation analysis indicated a positive and significant relationship between differentiation and self-compassion (r = 0.645, p < .01); a negative and significant relationship between differentiation and death anxiety (r = -0.411, p < .01); a positive and significant relationship between differentiation and self-acceptance (r = 0.700, p < .01); a negative and significant relationship between self-compassion and death anxiety (r = -0.352, p < .01); and a positive and significant relationship between self-compassion and self-acceptance (r = 0.846, p < .01). Path analysis revealed that the indirect path from differentiation to death anxiety through the mediating role of self-compassion was statistically significant (β = -0.629, p < .01). Furthermore, the indirect path from differentiation to self-acceptance through self-compassion was also statistically significant (β = 0.861, p < .01). Conclusion: Thus, the structural model of death anxiety and self-acceptance based on differentiation with the mediating role of self-compassion demonstrated a good fit among elderly individuals residing in daily care centers in Nowshahr and Chalous. These findings may serve as a basis for developing comprehensive therapeutic models to enhance psychological well-being among the elderly. Keywords: Death Anxiety, Self-Acceptance, Differentiation, Self-Compassion, Elderly.

This research work aims to present a robust deep learning framework by devising a deep learning-based ensemble method of YOLOv8 and EfficientNet. The suggested model is evaluated on the dataset collected from Kaggle, comprising 10,000 high-definition images of stems, leaves, and cut fruits of banana and papaya. These images are captured under different lighting conditions and thus expanded to 80,000 images. Authors have proposed an ensemble model comprising YoloV8 and EfficientNet as base deep learning models to enhance prediction and classification performance. Here, authors combine the merits of both models, i.e., speed of YoloV8 and the accuracy of EfficientNet, by putting a majority voting method in place. The final forecast is determined by majority voting, and EfficientNet is given higher significance in the situation of a tie owing to its enhanced accuracy. The proposed model presents a robust solution for agricultural disease management and demonstrates significant improvements in the detection of diseases in papaya and banana, opening avenues for its widespread employment in real life.