Below is a list of submissions received by the journal Ciencia y Tecnología that have been approved and are currently pending issue assignment.

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Cutting-Edge Migration Control: The Importance of Implementing Biometric Technology to Address Irregular Migration in Peru
Edison Rosas Ore

Thematic field: Information Technologies.

Abstract: This research aims to describe the importance of implementing biometric technology to address irregular migration in Peru. The research methodology was based on a qualitative approach, classified as basic because it seeks to generate new knowledge; descriptive, non-experimental, and cross-sectional, with a sample of 8 police officers from the Immigration Division. Data collection was carried out through a focus group. The results of this study reveal the deficient migration control procedures, the lack of compliance by authorities, and, above all, the absence of innovation in migration control processes. The study concludes that there is a clear need to develop a system integrating biometric technologies in order to adequately register and control migrants in irregular situations in Peru.
Keywords: migration control; irregular migration; border security; biometric technology.

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Detection of Data Quality Problems: A Quantitative Analysis in the Academic Field
Liliana Cuenca Pletsch, María José Ojeda, Germán Gaona & Verónica A. Bollati

Thematic field: Information technologies.

Abstract: Data quality and data governance is a key issue in organizations of all types. This article analyzes data quality problems in Argentinean public universities in the NEA. Using a conceptual model based on Root Causes of data quality problems, a survey was designed for students, teachers and non-teaching staff, with the aim of surveying perceptions about organizational, technical and cultural conditions that affect dimensions of data quality, such as consistency, accuracy and completeness. The analysis of the responses obtained made it possible to identify the presence of warning signs linked to multiple sources of information and subjective criteria in data generation, among others. The findings reveal a gap between the information available and the real needs of users, which limits effective decision making. In conclusion, improving data quality requires a comprehensive approach that combines organizational strategies, such as data governance, with investments in infrastructure and training.

Keywords: data quality; data governance; quantitative analysis; university.