The food insecurity and inadequate management of family farm production is a problem that per-sists today in all corners of the world. Therefore, the purpose of this study was to analyze the socioeconomic and agricultural production management factors associated with food insecurity in rural households in the Machángara river basin in the province Azuay, Ecuador. The information was collected through a survey applied to households that were part of a stratified random sample. Based on this information, the Latin American and Caribbean Household Food Security Measurement Scale (ELCSA) was constructed to estimate food insecurity as a function of socioeconomic factors and agricultural production management, through the application of a Binomial Logit model and an Ordinal Logit model, in the STATA^® 16 program. The results show that head house a married head of household, living in an informal house, having a latrine, producing medicinal or ornamental plants, and the relationship between expenses and income are significant variables that increase the probability of being food insecure. In this way, this research provides timely information to help public policy makers employ effective strategies to benefit rural household that are food vulnerable.

This study aims to explore the urban resilience strategies and public service innovations approaches adopted by the Shanghai Government in response to COVID-19 pandemic. The study utilized a combination of primary and secondary data sources, such as government reports, policy documents, and interviews with important individuals involved in the matter. The current research focused on qualitative data and examined the different aspects resilience, including infrastructure, economy, society, ecology, and organizations. The findings indicate that infrastructure resilience plays a crucial role in maintaining the stability and dependability of essential public facilities, achieved through online education and intelligent transportation systems. Implementing rigorous waste management and pollution control measures with a focus on ecological resilience has significantly promoted environmentally sustainable development. Shanghai city has achieved economic resilience by stabilizing its finances and providing support to businesses through investments in research, technology and education. Shanghai city has enhanced its organizational resilience by fostering collaboration across several sectors, bolstering emergency management tactics and enhancing policy execution.

Our study investigates the relationship between firm profitability, board characteristics, and the quality of sustainability disclosures, while examining the moderating effects of financial leverage and external audit assurance. A key focus is the distinction between Big 4 and non-Big 4 audit firms. Using data from Malaysia’s top 100 publicly listed organizations from 2018 to 2020, we analyze sustainability reports based on the Global Reporting Initiative (GRI) standards. Unexpectedly, our results indicate a negative association between firm profitability and board characteristics, challenging traditional assumptions. We find that non-Big 4 audit firms significantly enhance sustainability disclosure quality, contradicting the widely held belief in the superiority of Big 4 firms. Our finding introduces the “Big 4 dilemma” in the Malaysian context and calls for a reassessment of audit firm selection practices. Our study offers new perspectives on the strategic role of board composition and audit firm selection in advancing sustainability disclosures, urging Malaysian organizations to evaluate audit firms on criteria beyond the global prestige of Big 4 firms to improve sustainability reporting.

The goal of this work was to create and assess machine-learning models for estimating the risk of budget overruns in developed projects. Finding the best model for risk forecasting required evaluating the performance of several models. Using a dataset of 177 projects took into account variables like environmental risks employee skill level safety incidents and project complexity. In our experiments, we analyzed the application of different machine learning models to analyze the risk for the management decision policies of developed organizations. The performance of the chosen model Neural Network (MLP) was improved after applying the tuning process which increased the Test R² from −0.37686 before tuning to 0.195637 after tuning. The Support Vector Machine (SVM), Ridge Regression, Lasso Regression, and Random Forest (Tuned) models did not improve, as seen when Test R² is compared to the experiments. No changes in Test R²’s were observed on GBM and XGBoost, which retained same Test R² across different tuning attempts. Stacking Regressor was used only during the hyperparameter tuning phase and brought a Test R² of 0. 022219.Decision Tree was again the worst model among all throughout the experiments, with no signs of improvement in its Test R²; it was −1.4669 for Decision Tree in all experiments arranged on the basis of Gender. These results indicate that although, models such as the Neural Network (MLP) sees improvements due to hyperparameter tuning, there are minimal improvements for most models. This works does highlight some of the weaknesses in specific types of models, as well as identifies areas where additional work can be expected to deliver incremental benefits to the structured applied process of risk assessment in organizational policies.

This research aims to investigate the impact of knowledge-based human resource management (KBHRM) practices on organizational performance through the mediating role of quality and quantity of knowledge worker productivity (QQKWP). The data were collected from 325 employees working in different private universities of Pakistan by using convenience and purposive sampling techniques. The quantitative research technique was used to perform analysis on WarpPLS software. The result revealed that only knowledge-based recruiting practices have a positive and significant direct effect on organizational performance. While knowledge-based performance appraisal practices, training and development practices and compensation practices all were insignificant in this regard. However, through mediator QQKWP, the knowledge-based recruiting practices (KBRP), knowledge-based training and development (KBTD), and knowledge-based compensation practices (KBCP) all were positively and significantly influencing organizational performance but only knowledge-based performance appraisal (KBPA) was insignificant in this mediating relationship. Lastly, the current study provides useful insights into the knowledge management (KM) literature in the context of private higher educational institutes of developing countries like Pakistan. The future studies should consider the impact of KBHRM practices on knowledge workers’ productivity and firms’ performances in the context of public universities.

The explosion of information technology, besides its positive aspects, has raised many issues related to personal information and personal data in the network environment. Because children are vulnerable to abuse, fraud and exploitation, protecting children’s personal information and personal data is always of concern to many countries. From the concept and characteristics of personal information and personal data of children in Europe, the United States and Vietnam, it can be seen that children’s personal information and personal data protection is very necessary in every country today. This research focuses on the age considered a child, the child’s consent and his or her parental consent when providing and processing personal information or personal data of children under the laws of the EU, US and Vietnam. Therefore, the article proposes some recommendations related to the child’s consent and his or her parental consent in protecting children’s personal data in Vietnam.