The study has formulated the objective of synthesizing the extent to which technological barriers intervene in the transparency and effectiveness of public management (PM). Methodologically, the study was of a fundamental or basic nature, with a systematic review design, the databases of Scopus (369), SciELO (2), Web of Science (184) were explored, after the review process a set of 22 articles was available. The registration was made in an Excel table where the main data of the articles were included. 32% of the articles selected for the analysis of the evidence are from the period 2020, 27% were from 2022 and 18% from the year 2023; as far as origin is concerned, 14% of the articles come from Peru and 9% from Australia, Brazil, South Korea, Spain and Indonesia. In summary, the study points out that government institutions are making progress in digitizing and improving the citizen experience through electronic services, but they face challenges in areas such as resource management, the low adoption of advanced technologies such as blockchain and artificial intelligence, as well as the lack of transparency in PM. Despite this, it is highlighted that e-government improves citizen satisfaction, and the need to invest in digital innovation, training and overcoming technological barriers to achieve an effective transformation in state administration and promote a more inclusive and advanced society is emphasized.

This study delves into the concept of the “cultural bomb” within the framework of non-military defense empowerment strategies in Indonesia. This approach can potentially change society’s views and attitudes towards various security threats as a realization of strengthening the defense and security system of the universal people (Sishankamrata) per article 30 paragraph (2) of the 1945 constitution. By leveraging media, education, and information technology, the cultural bomb acts as a social weapon that operates powerfully in the “space of mind,” shaping behavior and actions nonviolently. The issue of cultural threats pertains to the infiltration and imposition of foreign cultural values and practices that undermine local traditions and national identity, leading to social fragmentation and weakness. This study proposes the concept of a “cultural bomb” as a policy framework to address and mitigate these cultural threats. The research employs a qualitative approach using the Delphi technique, engaging experts from cultural studies and defense strategies to reach a consensus on the strategic application of the cultural bomb. The results indicate that the cultural bomb can effectively strengthen national identity and awareness of national defense by promoting local values and cultural resilience, thus enhancing societal cohesion and mitigating the impact of foreign cultural influences. The paper outlines the components of a cultural bomb, analyzes its application in international contexts, and discusses its implications in efforts to strengthen national identity and foster a sense of national defense awareness. Focusing on the “war over space of mind” ideology, it introduces “cultural hacking” as a strategic initiative to address cultural power imbalances in the post-truth era.

Breast cancer was a prevalent form of cancer worldwide. Thermography, a method for diagnosing breast cancer, involves recording the thermal patterns of the breast. This article explores the use of a convolutional neural network (CNN) algorithm to extract features from a dataset of thermographic images. Initially, the CNN network was used to extract a feature vector from the images. Subsequently, machine learning techniques can be used for image classification. This study utilizes four classification methods, namely Fully connected neural network (FCnet), support vector machine (SVM), classification linear model (CLINEAR), and KNN, to classify breast cancer from thermographic images. The accuracy rates achieved by the FCnet, SVM, CLINEAR, and k-nearest neighbors (KNN) algorithms were 94.2%, 95.0%, 95.0%, and 94.1%, respectively. Furthermore, the reliability parameters for these classifiers were computed as 92.1%, 97.5%, 96.5%, and 91.2%, while their respective sensitivities were calculated as 95.5%, 94.1%, 90.4%, and 93.2%. These findings can assist experts in developing an expert system for breast cancer diagnosis.