Universidad Continental

This research addresses the critical need to study human capital and organizational design in the construction industry. To overcome the scarcity of literature on the functionality and responsiveness of organizational charts, this study carries out a comparative analysis of large-scale construction projects in Chile and Peru. Using a multiple-case study approach and a Complex Adaptive Systems (CAS) framework, the research examined how organizational structures configure and adapt to local contexts through semi-structured interviews, exploring the influence of external factors such as regulations, the market, and culture on the design of organizational charts. The results reveal that while Peruvian structures tend toward centralization influenced by cultural factors, Chilean structures exhibit greater horizontality. Furthermore, Lean philosophy emerged as a key contextual factor that facilitates organizational adaptability in complex environments. The study concludes that organizational adaptability is not merely a product of formal design, but an emergent property driven by the interaction between formal roles and management practices. These findings contribute to a deeper understanding of how construction firms can configure their structures to better respond to project complexity. For future research, it is proposed to explore the correlation between the complexity of organizational charts and project performance, using quantitative and qualitative indicators, and analyzing the evolution of organizational structures throughout the project life cycle. In addition, it is suggested to investigate the impact of digitalization and agile methodologies, as well as to conduct longitudinal studies to assess the resilience and adaptability of organizational charts to regulatory and market changes.

Parastomal hernia (PSH) is a common complication after stoma creation and may present emergently with obstruction, incarceration, or ischemia. While minimally invasive surgery (MIS, laparoscopic or robotic) shows benefits in elective PSH repair, data in emergencies are limited. This study compared outcomes following open versus MIS emergent PSH repair. In this retrospective cohort using ACS-NSQIP, patients undergoing emergent open, laparoscopic, or robotic PSH repair were identified. Surgical approach was categorized as open or MIS. A 3:1 propensity score–matched analysis balanced demographic, comorbidity, physiologic, laboratory, and operative variables. Primary outcome was 30-day overall postoperative complications; secondary outcomes included wound, medical, and surgical complications, return to the operating room (OR), length of stay, and readmission. Multivariate logistic regression identified predictors of complications. Of 303 patients (249 open, 54 MIS), 192 were analyzed after matching (144 open, 48 MIS) with balanced baseline characteristics. MIS repair had lower overall complications than open repair (22.9

The current mental health system faces an important yet not well-understood problem, which involves addictions that do not respond to treatment. The cases present with ongoing substance abuse, multiple treatment failures, psychiatric conditions, social instability, and work-related problems. The absence of standardized operational criteria creates problems with immediate patient identification, comparing research study results, and healthcare providers' efforts to create structured treatment plans. The article combines existing data about treatment-resistant addiction to create an initial framework that explains its medical characteristics, brain functions, social aspects, and treatment approaches. The research uses depression and schizophrenia treatment-resistant frameworks to develop functional criteria, which include multiple treatment failures, ongoing disability, complex medical conditions, and inadequate response to optimal treatment protocols. Healthcare providers can identify actual refractoriness using established criteria that help distinguish it from pseudo-resistance, which occurs due to incorrect medical diagnoses, insufficient medication exposure, or non-compliance with treatment instructions. The research presents typical medical signs that appear in people who do not respond to substance use treatment through their impulsive behavior, their inability to identify emotions, their background of trauma, their presence of multiple psychiatric conditions, and their exposure to poverty and prison life. The review evaluates present-day pharmacological treatments that combine GLP-1 receptor agonists with ketamine and cannabidiol, partial agonists, and neuromodulation methods (rTMS and tDCS), as well as psychotherapeutic approaches (ACT, DBT, MBRP, and CRA) to improve treatment outcomes. We propose an operational system that integrates diagnostic evaluation with medication treatment and psychotherapy services, brain function assessments, and social assistance programs. This model is focused on a team-based approach aimed at delivering personalized medical care. Patients' non-adherence is considered as a clinical issue rather than a factor of social labeling or exclusion. Empirical testing is needed to validate the proposed evaluation criteria, as well as treatment effectiveness in real-world settings should be confirmed to approve accessible and advanced care for all patients.

The rapid integration of Industry 4.0 technologies into non-computer engineering curricula presents a significant pedagogical challenge: avoiding a “one-size-fits-all” approach. While Project-Based Learning (PBL) is widely advocated for teaching Internet of Things (IoT), little research addresses how students from different engineering branches—specifically Industrial, Environmental, and Electrical—respond to identical technical requirements. This study evaluates the deployment of ESP32-based IoT solutions for local agriculture and beekeeping problems in the Peruvian Andes, analyzing the performance and perception of three distinct student cohorts (Total N = 95). Results indicate a significant divergence in learning outcomes and satisfaction. The cohort predominantly composed of Industrial Engineering students (NRC-33563) demonstrated lower adherence to technical code modularization (88% vs. 97%) and lower overall course recommendation rates compared to the mixed cohorts (NRC-33562/33561), who reported higher engagement with the hardware implementation. These findings suggest that while Environmental and Electrical engineering students naturally align with the sensing and actuation layers of IoT, Industrial engineering students may require a curriculum that emphasizes process optimization and data analytics over raw firmware development. We propose a differentiated pedagogical framework to maximize engagement and competency acquisition across diverse engineering disciplines.

Global agri-food systems face a critical conflict between the need to feed a growing population and the imperative to mitigate its substantial environmental impact, including 23% of global greenhouse gas emissions and 70% of freshwater withdrawals. This bibliometric review maps the scientific landscape of Machine Learning (ML) research applied to sustainable agri-food systems. Using a structured bibliometric protocol, we analyzed 648 scientific documents from Scopus (2010–2025) to map the evolution, collaborative networks, and thematic trends in this domain. Results reveal a field that has grown exponentially until 2021, primarily driven by contributions from Computer Science (26%) and Engineering (21%), with key publications in journals such as Computers and Electronics in Agriculture (22 papers, 2631 citations). While China and India lead in productivity (80% of top authors), high-impact research remains strongly linked to international collaborations with institutions in the U.S. and EU. Current ML efforts focus on technical optimization—such as precision irrigation, pest detection, and yield prediction—but fall short in addressing social equity and climate resilience. The study concludes that while ML holds significant promise for sustainable agri-food processing and system optimization, future progress depends on overcoming fragmented regional collaborations and integrating holistic frameworks, such as life-cycle assessment, to ensure resilient and equitable food systems.