Manyanda tradition, a tradition of taking over social roles after death, in addition to successfully maintaining social continuity in the family structure, is also a potential capital in strengthening social cohesion. However, this context has not been discussed comprehensively in previous studies so it is very important to explain. In addition to responding to the shortcomings of previous studies, this study also aims to explain the mechanisms, factors and implications of the practice of this tradition as a reflection of social cohesion based on customary and religious values. By using a qualitative descriptive case study approach, this study shows three important findings. First, the spontaneity of the community and traditional leaders when hearing the news of death and social activities forty days afterwards. Second, the dominance of spiritual and cultural factors in addition to social and structural factors that encourage the community to preserve this tradition. Third, the Manyanda tradition has implications for strengthening the community’s commitment and belief in the meaning of death, the importance of a replacement figure who takes over social roles and strengthens the tribal identity of the Nagari (local village) community. This study recommends the importance of this tradition to be preserved as the root of social cohesion.

Cases of human trafficking are becoming more prevalent and represent grave abuses of human rights. Both locally and internationally, victims of human trafficking run the danger of being exploited, violent, or infected with contagious illnesses. The Indonesian government has not fully complied with the minimal criteria for safeguarding victims of human trafficking, notwithstanding Law Number 21 of 2007 for the Eradication of the Crime of Human Trafficking. Human rights restoration and respect for victims of human trafficking must be given priority in the implementation of legal protection for these individuals. To strengthen and increase the security of victims’ rights in the future, this study intends to conduct a thorough analysis of the humanism approach model and policies for safeguarding victims of human trafficking. This research uses an empirical technique to support its normative legal analysis. Primary and secondary legal sources are used in this research. The study’s findings show that the protection provided by humanist criminal law for victims of human trafficking is founded on humanitarian principles that derive from the divine principles found in the Pancasila ideology. There are additional requirements for punishment, such as its purpose, its ability to serve as therapy, and its determination to reflect the victim’s and society’s sense of justice. This criminal law is founded on the principles of legality and balance.

The recession cone and recession function are very important research objects in Convex Analysis. They have extensive applications in the optimization theory. Firstly, we study the properties of the recession cone and recession function. The positive homogeneity and subadditivity of recession function are mainly discussed. And the different methods are considered to prove these properties. Secondly, we discuss the unboundedness of the convex sets and convex functions by using recession cone and recession function.

Nomophobia, the anxiety experienced when individuals are separated from their mobile phones, is becoming increasingly prevalent in modern workplaces. This study investigates the role of organizational commitment in mitigating nomophobia, with a focus on the mediating influence of the ethical environment. Data were collected from 600 participants and analyzed using Structural Equation Modeling (SEM). The findings show that a strong sense of organizational commitment significantly reduces nomophobia among employees. Additionally, an ethical environment within organizations further mitigates this anxiety by fostering a workplace culture that encourages psychological well-being. This research provides practical insights for organizations looking to reduce the psychological strain associated with digital dependency, emphasizing the importance of both commitment and a strong ethical climate.

This research aims to examine the structural relationships between the dimensions of workation attachment, workationer power, the dimensions of workation relationship quality, and workation intention. It demonstrates that the proposed model aligns well with the collected data based on a convenience sample comprising 494 workationers in Bangkok using structural equation modeling. The analysis outcomes contribute to the tourism marketing theory by providing additional insights into the dimensions of workation attachment, workationer power, the dimensions of workation relationship quality, and workation intention. The findings from this study can aid workation managers in formulating and executing market-oriented service strategies to enhance the dimensions of workation attachment, workationer power, and workation relationship quality and foster workation intention.

In this paper, we assess the results of experiment with different machine learning algorithms for the data classification on the basis of accuracy, precision, recall and F1-Score metrics. We collected metrics like Accuracy, F1-Score, Precision, and Recall: From the Neural Network model, it produced the highest Accuracy of 0.129526 also highest F1-Score of 0.118785, showing that it has the correct balance of precision and recall ratio that can pick up important patterns from the dataset. Random Forest was not much behind with an accuracy of 0.128119 and highest precision score of 0.118553 knit a great ability for handling relations in large dataset but with slightly lower recall in comparison with Neural Network. This ranked the Decision Tree model at number three with a 0.111792, Accuracy Score while its Recall score showed it can predict true positives better than Support Vector Machine (SVM), although it predicts more of the positives than it actually is a majority of the times. SVM ranked fourth, with accuracy of 0.095465 and F1-Score of 0.067861, the figure showing difficulty in classification of associated classes. Finally, the K-Neighbors model took the 6th place, with the predetermined accuracy of 0.065531 and the unsatisfactory results with the precision and recall indicating the problems of this algorithm in classification. We found out that Neural Networks and Random Forests are the best algorithms for this classification task, while K-Neighbors is far much inferior than the other classifiers.