The advancement of informatization has significantly affected many sectors due to e-commerce, rendering the existing Financial Model (FM) inadequate for e-commerce customers. Artificial Intelligence (AI) substantially enhances the financial accounting capabilities and resource integration of systems using mobile networks. The insufficient FM of e-commerce tools and issues such as the administration of funds have significantly obstructed the advancement of standardized financial procedures, as the FM structure impacts managers' statistical evaluation of financial information. This research examines the financial hazards, management issues, and underlying causes of these issues inside e-commerce systems, subsequently employing AI to assess the FM operations of these websites in Peru. This research uses a Machine Learning (ML) approach to examine the clustering centers of FM information as well as the privacy aspect of the FM. This study ultimately presents ways for optimizing and constructing FMs to enhance data security and capital administration in e-commerce financing, hence fostering the continued growth of e-commerce systems using mobile networks. The experimental findings indicate that the FM’s classification value and security aspect for e-commerce systems progressively enhance under the ML. The average classification value is 1.23, whereas the average risk factor is around 1.41. AI and ML improve financial accounting requirements and the long-term growth of e-commerce systems in Peru.

As the user base expands, social network data becomes more intricate, making analyzing the interconnections between various entities challenging. Various graph visualization technologies are employed to analyze extensive and intricate network data. Network graphs inherently possess intricacy and may have overlapping elements. Graph clustering is a basic endeavor that aims to identify communities or groupings inside networks. Recent research has mostly concentrated on developing deep learning techniques to acquire a concise representation of graphs, which is then utilized with traditional clustering methods such as k-means or spectral clustering techniques. Multiplying these two-step architectures is challenging and sometimes results in unsatisfactory performance. This is mostly due to the lack of a goal-oriented graph encoding developed explicitly for the clustering job. This work introduces a novel Deep Learning (DL) method called Deep Attentional Implanted Graph Clustering (DAIGC), designed to achieve goal-oriented clustering. Our approach centers on associated graphs to thoroughly investigate both aspects of data in graphs. The proposed DAIGC technique utilizes a Graph Attention Autoencoder (GAA) to determine the significance of nearby nodes about a target node. This allows encoding a graph's topographical structure and node value into a concise representation. Based on this representation, an interior product decoder has been trained to rebuild the graph structure. The performance of the proposed approach has been evaluated on four distinct types and sizes of real-world intricate networks, varying in vertex count from 𝑁=102 𝑡𝑜 𝑁=107. The performance of the suggested methods is evaluated by comparing them with two established and commonly used graph clustering techniques. The testing findings demonstrate the effectiveness of the proposed method in terms of processing speed and visualization compared to the state-of-the-art algorithms.

Assuring the safe and effective operation of a company's technological infrastructure is an essential part of business management. Monitoring, administering, and troubleshooting the many components and systems of the network are all part of the process. Additionally, it is the responsibility of business administrators to find methods to enhance the existing system, as well as to make certain that all resources are distributed in such a manner that their use may be maximized. Management of the business is a key component of the technological infrastructure of any organization, and it is of the utmost importance that it be carried out effectively in order to guarantee a safe and effective operation. It is essential for network operators to discover strategies to improve the energy efficiency of their networks without adversely affecting the quality of service (QoS) for reasons related to both cost and sustainability. In this research, an in-depth analysis of business management techniques for effective resource utilization is presented. This analysis begins with the design of the network and continues all the way through to the delivery of accurate data. An Energy Efficient Network (EEN) must be able to give programmability and flexibility to network infrastructures, as well as the ability to operate networks in a rapid way and provide operators with more control. It is impossible to ignore energy throughout the process of meeting the needs and requirements of network services, especially when taking into consideration the consequences on the long-term viability of both the environment and businesses. Energy efficiency in both current and future networks is the topic of discussion in this research. The findings indicate that energy efficient networks are successful in overcoming the present issues that stand in the way of the application of energy efficiency techniques, also the discussed model are effective in addressing some of the obstacles that are encountered by small and medium-sized businesses.

The objective of this study was to determine the relationship between institutional planning and the budget execution of expenditures in the local governments of Tumbes. The research was conducted under a quantitative, basic approach, with a non-experimental and correlational design. The population consisted of public servants from municipal entities, and data were collected through a survey using a structured questionnaire. The results showed a significant relationship between institutional planning and budget execution of expenditure, with a Spearman's rho coefficient of 0.732, indicating that adequate planning contributes to the efficient use of public resources. The study concludes that strengthening institutional planning processes improves the quality of budget management in municipal entities in Tumbes, favoring the achievement of institutional goals and objectives.

El objetivo de este artículo es analizar las características del modelo socioformativo para la construcción y el desarrollo de sociedades sostenibles en la formación universitaria. El artículo señala que en Latinoamérica las universidades necesitan rediseñar su currículo, incluyendo el enfoque socioformativo, para formar individuos capaces de resolver problemáticas emergentes como el daño ambiental y la pérdida de ética. La socioformación promueve la mejora integral del estudiante, la resolución de problemas contextuales y el pensamiento crítico. Además, permite articular distintos campos del saber para abordar problemas interdisciplinariamente; enfatiza competencias emocionales y sociales, la reflexión y la participación del estudiante en su aprendizaje; y promueve la identificación y resolución colaborativa de problemas reales mediante proyectos formativos. El análisis concluye que la socioformación genera resultados en estudiantes y docentes, fortalece el funcionamiento universitario y forma individuos que apliquen sus conocimientos para resolver problemas de manera ética.

Objective: The study examines how labor competencies encompass technical knowledge, soft skills, attitudes, and values that are fundamental for the modernization and improvement of local public management. The research aligns with Sustainable Development Goal (SDG) 16, emphasizing effective, accountable, and inclusive institutions. Method: The methodology is a systematic literature review using academic databases such as Scopus, Web of Science, SciELO, and JSTOR, with specific inclusion and exclusion criteria for articles published between 2019 and 2024. Results and Discussion: The results reveal a significant gap between the required competencies and those currently possessed by many Peruvian local government officials, identifying challenges such as a shortage of trained technical cadres, clientelistic practices in hiring, lack of investment in continuous training, difficulties in retaining talent, and weaknesses in competency assessment. Conclusion: The study proposes a framework for implementing competency systems tailored to Peru’s local realities, supporting the design of development programs, recruitment systems, and performance evaluations. It highlights the potential of aligning these initiatives with SDG 16 to enhance institutional effectiveness and public trust. Research Implications: The article suggests future lines of research, including comparative studies and analysis of the impact of technology on competency management in local governments.