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To analyze the relationship between burnout syndrome, cognitive functions, and sBDNF (Serum Brain-derived Neurotrophic Factor) in Mexican nurses. A descriptive cross-sectional design was used. This study target staff nurses working in hospitals in Guanajuato, México. Demographic and working condition data were collected via questionnaire. The Maslach Burnout Inventory (MBI) was used to evaluate burnout. A blood sample were collected and processed by ELISA technique to measure sBDNF. Finally, the General Cognitive Assessment (CAB) of the Cognifit© neuropsychological battery was used to evaluated cognitive functions. Findings showed that there are sociodemographic characteristics and working conditions associated with burnout syndrome among nurses. Furthermore, the data demonstrated a significant decrease in sBDNF levels in burnout nurses and a negative correlation between BDNF levels and burnout syndrome. Additionally, these burnout nurse also revealed significant cognitive impairment in reasoning, memory, and attention as well as total scores of CAB. Interestingly, we found a positive correlation between sBDNF levels and the cognitive deficits in burnout nurse. Reduced BDNF levels could be a biological indicator or part of the pathological process of burnout, which could affect cognitive abilities. Reduced cognitive function in nurses has relevant implications and emphasizes the need for specialized preventive strategies because nurses make clinical decisions concerning their patients, whose situations are constantly changing.

The use of UAV (unmanned aerial vehicle) platforms and photogrammetry in bathymetric surveys has been established as a technological advancement that allows these activities to be conducted safely, more affordably, and at higher accuracy levels. This study evaluates the error levels obtained in photogrammetric UAV flights, with measurements obtained in surveys carried out in a controlled water body (pool) at different depths. We assessed the relationship between turbidity and luminosity factors and how this might affect the calculation of bathymetric survey errors using photogrammetry at different shallow-water depths. The results revealed that the highest luminosity generated the lowest error up to a depth of 0.97 m. Furthermore, after assessing the variations in turbidity, the following two situations were observed: (1) at shallower depths (not exceeding 0.49 m), increased turbidity levels positively contributed error reduction; and (2) at greater depths (exceeding 0.49 m), increased turbidity resulted in increased errors. In conclusion, UAV-based photogrammetry can be applied, within a known margin of error, in bathymetric surveys on underwater surfaces in shallow waters not exceeding a depth of 1 m.

This paper aims to identify typologies of Latin American cities based on socioeconomic urban environment patterns. We used census data from 371 urban agglomerations in 11 countries included in the SALURBAL project to identify socioeconomic typologies of cities in Latin America. Exploratory factor analysis was used to select a set of variables, and finite mixture modelling (FMM) was applied to identify clusters to define the typology of cities. Despite the heterogeneities among the Latin American cities, we also found similarities. By exploring intersections and contrasts among these clusters, it was possible to define five socioeconomic regional typology patterns. The main features of each one are low-education cities in Northeast Brazil; low-unemployment cities in Peru and Panama; high-education cities in Argentina, Chile, Colombia, Costa Rica, Nicaragua and Mexico; high female labor participation, with high primary education in Argentina and low primary education in Brazil; and low female labor participation and low education in Brazil, Colombia, El Salvador, Guatemala, and Mexico. Identifying clusters of cities with similar features underscores understanding of the urban social and economic development dynamics and assists in studying how urban features affect health, the environment, and sustainability.

Based on Life Cycle Assessment (LCA) and ISO standards, we compared the global environmental sustainability (ES) of two technologies that process the organic fraction of municipal solid waste (OFMSW) in Mexico. The first technology was a biorefinery (BRF) known as HMEZSNN-BRF (abbreviation for Hydrogen-Methane-Extraction-Enzyme-Saccharification/Nanoproduction Biorefinery); it produces the gas biofuels hydrogen (H) and methane (M), organic acids (E), enzymes (Z), saccharified liquors (S), and bionanobioparticles (BNBPs) in a nanoproduction stage (NN). The second technology was incineration with energy recovery (IER). An LCA was performed with a functional unit (FU) of 1000 kg of OFMSW. The BRF generates 166.4 kWh/FU (600 MJ) of net electricity, along with bioproducts such as volatile organic acids (38 kg), industrial enzyme solution (1087 kg), and BNBPs (40 kg). The IER only produces 393 net kWh/FU electricity and 5653 MJ/FU heat. The characterization potential environmental impacts (PEIs) were assessed using SimaPro software, and normalized PEIs (NPEIs) were calculated accordingly. We defined a new variable alpha and the indices σ-τ plane for quantifying the ES. The higher the alpha, the lower the ES. Alpha was the sum of the eighteen NPEIs aligned with the ISO standards. The contributions to PEI and NPEI were also analyzed. Four NPEIs were the highest in both technologies, i.e., freshwater and marine ecotoxicities and human non-carcinogenic and carcinogenic toxicities. For the three first categories, the NPEI values corresponding to IER were much higher than those of the BRF (58.6 and 8.7 person*year/FU freshwater toxicity; 93.5 and 13.6 marine ecotoxicity; 12.1 and 1.8 human non-carcinogenic toxicity; 13.7 and 13.9 human carcinogenic toxicity, for IER and the BRF, respectively). The total α values were 179.1 and 40.7 (person*yr)/FU for IER and the BRF, respectively. Thus, the ES of IER was four times lower than that of the BRF. Values of σ = 0.592 and τ = −0.368 were found; the point defined by these coordinates in the σ-τ plane was located in Quadrant IV. This result confirmed that the BRF in this work is more environmentally sustainable (with restrictions) than the IER in Mexico for the treatment of the OFMSW.

Background The COVID-19 health crisis rapidly escalated into a broader social and economic emergency, affecting physical and mental health beyond the disease itself and placing a disproportionate burden on vulnerable populations. This study investigates the determinants of poor mental health related to COVID-19 among urban-poor homemakers residing in a social housing villa located on the outskirts of Santiago de Chile, a metropolitan city characterized by pronounced sociospatial segregation. Methods Capitalizing on the ongoing RUCAS cohort study, we introduced a COVID-19 module into the biannual RUCAS survey to assess the associations between COVID-19-related social determinants of poor mental health and symptoms of depression during and after the 2020 lockdown among 413 homemakers. We describe the prevalence and distribution of COVID-19-related social determinants of poor mental health, and longitudinally examine their associations with depressive symptoms, measured using the Patient Health Questionnaire-2 (PHQ-2). Modified Poisson regression models were used to estimate prevalence ratios (PRs) and their corresponding 95% confidence intervals, including an interaction between the independent variable and the measurement wave for the longitudinal assessment. Results During lockdown, symptoms of depression among homemakers were most strongly associated with students in the household facing difficulties in completing schoolwork during homeschooling (PR: 2.62; 1.60–4.30). After lockdown, the strongest associations were observed with employment (being out of the labor force (PR: 1.59; 1.04–2.44); receiving reduced income from work (PR: 1.95; 1.22–3.11)), and the financial situation of the household: indebtedness (wave 4: acquisition of new debts, PR: 1.48; 1.04–2.11; wave 5: low debt-payment capacity, PR: 1.44; 1.01–2.07), and food insecurity (wave 4, PR: 1.92; 1.38–2.65; wave 5, PR: 1.59; 1.12–2.26). Household conflicts over space were associated with depressive symptoms in both waves (wave 4, PR: 1.43; 1.01–2.02; wave 5, PR: 1.74; 1.23–2.46). No association was found with being or living with a COVID-19 case. Conclusions In addition to the central role of financial stressors in shaping mental health outcomes during the COVID-19 pandemic, our results underscore the importance of home-schooling challenges as a critical source of psychological distress among peripheralized homemakers. These findings offer valuable insights for enhancing multisectoral preparedness in future crises, emphasizing the need to integrate social, economic, and educational support strategies for peripheralized urban-poor populations and to prevent or mitigate the deepening of existing social inequalities.

The introduction of artificial intelligence methods and techniques in the construction industry has fostered innovation and constant improvement in the automation of monitoring and control processes at construction sites, although there are areas where more studies still need to be conducted. This paper proposes a method to determine the criticality of cracks in concrete samples. The proposed method uses a previously trained YOLOv4 neural network to identify concrete cracks. Then, the region of interest, determined by the bounding box resulting from the neural network model classification, is extracted. Finally, the extracted image is converted to negative grayscale to quantify the number of white pixels above a certain threshold, automatically allowing the system to characterize the fracture’s extent and criticality. The classification module reached a veracity between 98.36% and 99.75% when identifying five concrete crack types of failures in 1132 images. A qualitative analysis of the results obtained from the characterization module shows a promising alternative to evaluate the criticality of concrete cracks.

The combination of light detection and ranging (LiDAR) sensors and unmanned aerial vehicle (UAV) platforms have garnered considerable interest in recent years because of the wide range of applications performed through the generation of point clouds, such as surveying, building layouts and infrastructure inspection. The attributed benefits include a shorter execution time and higher accuracy when surveying and georeferencing infrastructure and building projects. This study seeks to develop, integrate and use a LiDAR sensor system implemented in a UAV to collect topography data and propose a procedure for obtaining a georeferenced point cloud that can be configured according to the user’s needs. A structure was designed and built to mount the LiDAR system components to the UAV. Survey tests were performed to determine the system’s accuracy. An open-source ROS package was used to acquire data and generate point clouds. The results were compared against a photogrammetric survey, denoting a mean squared error of 17.1 cm in survey measurement reliability and 76.6 cm in georeferencing reliability. Therefore, the developed system can be used to reconstruct extensive topographic environments and large-scale infrastructure in which a presentation scale of 1/2000 or more is required, due to the accuracy obtained in the work presented.

BackgroundHospitals are complex and dynamic facilities that house vulnerable people, medical equipment, and hazardous materials, making them susceptible to fires and explosions with potentially catastrophic effects. This study aims to understand and address fire and explosion risks in hospital environments using biomedical engineering, safety, and fire prevention concepts to develop effective mitigation strategies.Materials and MethodsThe study employed a mixed-methods approach, integrating quantitative and qualitative techniques. Data from an insurance and reinsurance company in Peru were analyzed, and surveys, data analysis, and interviews were conducted. The preferred reporting items for systematic review and meta-analysis (PRISMA) methodology was also applied to conduct a thorough literature review.ResultsThe results revealed the most frequent risk factors in hospital settings and identified the most effective mitigation strategies. Implementing these strategies resulted in a considerable decrease in the incidences of fires and explosions.DiscussionThe research provides a comprehensive explanation of hospital fire and explosion risks and proposes evidence-based strategies to improve the safety. These results underscore the relevance of biomedical engineering in managing risks within hospital settings. Despite certain limitations, the study lays a firm foundation for future research aimed at improving hospital safety.