This study aimed to explore the indirect effects of appearance-related anxiety (ARA) on Instagram addiction (IA) through sequential mediators, namely social media activity intensity (SMAI) and Instagram feed dependency (IFD). The study also aimed to provide theoretical explanations for the observed relationships and contribute to the understanding of the complex interplay between appearance-related concerns, social media usage, and addictive behaviors in the context of IA. A sample of 306 participants was used for the analysis. The results of the sequential mediation analysis (SMA) revealed several important findings. Firstly, the mediation model demonstrated that SMAI mediated the relationship between ARA and IA. However, there was no direct relationship observed between ARA and SMAI. Secondly, the analysis showed that IFD acted as a second mediator in the relationship between ARA and IA. Both ARA and SMAI had significant direct effects on IA, indicating their individual contributions to addictive behaviors. Furthermore, the total effect model confirmed a positive relationship between ARA and IA. This finding suggests that ARA has a direct influence on the development of IA. The examination of indirect effects revealed that ARA indirectly influenced IA through the sequential mediators of SMAI, IFD, and ultimately IA itself. The completely standardized indirect effect of ARA on IA through these mediators was found to be significant. Overall, this study provides evidence for the indirect effects of ARA on IA and highlights the mediating roles of SMAI and IFD. These findings contribute to our understanding of the psychological mechanisms underlying the complex relationship between appearance-related concerns, social media usage, and the development of IA.

This study conducts a comparative analysis of various machine learning and deep learning models for predicting order quantities in supply chain tiers. The models employed include XGBoost, Random Forest, CNN-BiLSTM, Linear Regression, Support Vector Regression (SVR), K-Nearest Neighbors (KNN), Multi-Layer Perceptron (MLP), Recurrent Neural Network (RNN), Bidirectional LSTM (BiLSTM), Bidirectional GRU (BiGRU), Conv1D-BiLSTM, Attention-LSTM, Transformer, and LSTM-CNN hybrid models. Experimental results show that the XGBoost, Random Forest, CNN-BiLSTM, and MLP models exhibit superior predictive performance. In particular, the XGBoost model demonstrates the best results across all performance metrics, attributed to its effective learning of complex data patterns and variable interactions. Although the KNN model also shows perfect predictions with zero error values, this indicates a need for further review of data processing procedures or model validation methods. Conversely, the BiLSTM, BiGRU, and Transformer models exhibit relatively lower performance. Models with moderate performance include Linear Regression, RNN, Conv1D-BiLSTM, Attention-LSTM, and the LSTM-CNN hybrid model, all displaying relatively higher errors and lower coefficients of determination (R²). As a result, tree-based models (XGBoost, Random Forest) and certain deep learning models like CNN-BiLSTM are found to be effective for predicting order quantities in supply chain tiers. In contrast, RNN-based models (BiLSTM, BiGRU) and the Transformer show relatively lower predictive power. Based on these results, we suggest that tree-based models and CNN-based deep learning models should be prioritized when selecting predictive models in practical applications.

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.

Foodborne diseases are a global health problem. Every year, millions of people die worldwide from these diseases. It has been determined that the high prevalence of these diseases is related to unfavorable socioeconomic conditions of the population. In this study, the relationship between foodborne diseases and socioeconomic conditions of the population was determined using principal component analysis as a multivariate statistical analysis technique. In this study, the socioeconomic variables of each Ecuador province and the prevalence of foodborne diseases (hepatitis A, salmonella, shigellosis and typhoid fever) during the years 2018 and 2019 were considered. The results show the relationship between foodborne diseases and the socioeconomic conditions of the population, as well as identifying regions more vulnerable to present high levels of prevalence of foodborne diseases, thus facilitating the implementation of social investment programs to reduce the prevalence of these diseases.

This paper aims to analyze the impact of access to Information and Communication Technologies (ICT) on the private returns to higher education (HE) focusing on gender inequality in 2020. Methodology: To evaluate the above impact a set of Mincerian equations will be estimated. The proposed approach mitigates biases associated with self-selection and individual heterogeneity. Data: The database comes from the National Household Income and Expenditure Survey (Encuesta Nacional de Ingresos y Gastos de los Hogares, ENIGH) from 2020. Results: Empirical evidence suggests that individuals that have HE have a positive and greater impact on their salary income compared to those with a lower educational level, being women that do not have access to ICT those with the lowest wage return. Policy: Access to ICT should be considered as one of the criteria that integrate social deprivation in the measurement of multidimensional poverty. Likewise, it is necessary to design public policies that promote the strengthening and creation of educational and/or training systems in technological matters for women. Limitations: No distinction was made between individuals that graduated from public or private schools, nor was income from sources other than work considered. Originality: This investigation evaluates the impact of access to ICT on the returns to higher education in Mexico, in 2020, addressing gender disparity.

This article addresses the pressing issue of training and mediation for conflict resolution among employees within a corporate setting. Employing a methodology that includes literature analysis, comparative studies, and surveys, we explore various strategies and their effectiveness in mitigating workplace conflicts. Through a comprehensive comparison with metrics and conclusions from other scholarly works, we provide a nuanced understanding of the current landscape of conflict resolution practices. As a result of our research, we implemented a tailored training program focused on conflict resolution for employees within a mobile company, alongside the development of a competency framework designed to enhance conflict resolution skills. This framework comprises five integral components: emotional, operational, motivational, behavioral, and regulatory. Our findings suggest that training in each of these competencies is essential for fostering a healthy workplace environment and must be integrated into organizational practices. The importance of this initiative cannot be overstated; effective conflict resolution skills are not only vital for individual employee wellbeing but also crucial for the overall efficiency and productivity of the organization. By investing in these competencies, companies can reduce turnover, enhance team cohesion, and create a more positive and collaborative workplace culture.