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Copyright of Revista Digital de Derecho Administrativo is the property of Universidad Externado de Colombia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Schedule conformance depends on construction activities starting and finishing on time. However, construction activities are often delayed because construction flows necessary to start their execution are unavailable. Construction flows can be classified into: labor, equipment, workspace, materials, precedence, information, and external. Current construction models do not formally represent, measure, and track all flow types. Hence, field managers lack formal methods for managing flows and instead rely on their intuition and experience managing them. This paper presents a construction model based on activities and flows called the activity–flow model (AFM). The AFM formally represents the activities, the flows, and their interactions. The AFM was validated prospectively through its implementation on three building projects that were in different phases (foundations, core and shell, and finishing), at different geographic locations (Bogota, Copenhagen, and Lima), and used different planning and control methods (master schedule and weekly planning, last planner system, and location-based management system). The AFM was able to represent all the activities and flows in the test projects, track variations of activities and flows, and quantify activity and flow variability. Field managers can use the metrics enabled by the AFM to plan and control the project. Such proactive flow management can help field managers improve flow readiness, which should reduce activity delays and improve schedule conformance. [ABSTRACT FROM AUTHOR]

This paper attempts to ethnographically reflect on how material vulnerability is conceived and considered in practices of communal sustenance of architectures in trouble. Drawing on theories of maintenance, care, and repair, we will dwell on the learnings cultivated during a socio-material process that unfolded in response to the burning of a communal library in the southern periphery of the city of Bogotá. The collective practices of sustenance deployed to take care of the threatened architecture help us bring to the forefront questions of (I) distributed agency and ecology of practices; (II) unanticipated future and alternative temporalities; (III) creative and generative responses to damage or conflict; as well as (V) tentative and (IV) ‘opening of black boxes’ methods. Through the ethnographic analysis of the disassembling process, we will problematize the socio-material ecosystems needed to sustain lives in a ‘broken world’. [ABSTRACT FROM AUTHOR]

Introduction: The stigma and social discrimination against cisgender women sex workers lead many of them to live in conditions characterized by social inequality, marginalization, persecution, and limited opportunities for sexual health literacy. Consequently, they are often compelled to establish a framework of preventive beliefs with little scientific validity, which they use to identify, mitigate, or avoid sexual health risks arising from their interactions with clients. This study investigates the sexual health beliefs that influence self-care practices aimed at preventing sexually transmitted infections among cisgender women sex workers in Colombia. Methods: We conducted a qualitative study framed in Hermeneutic Phenomenology. In-depth interviews and discussion groups were conducted with 34 cisgender women sex workers over 18 years of age in the center of the cities of Bogotá and Barranquilla in Colombia. Results: Seven themes emerged from the reflective and inductive thematic analysis of the narratives: (1) popular habits for the prevention of contagion, (2) ocular assessment of genitals, (3) condom lubrication, (4) suspicion of a hidden infection in the client, (5) saliva and oral contact as a source of contagion, (6) avoidance of semen contact, (7) and trust in God as protection. Conclusions: The findings reflect the need for health and social professionals to promote participatory and inclusive cooperation with sex workers to update the framework of preventive beliefs that help them guide sexual health self-care with autonomy and self-efficacy, strengthening favorable beliefs and negotiating unfavorable ones. It is also essential to have a sex worker-informed sexual health policy that guides the promotion of sexual health that is sensitive to the needs and consistent with the risks of sex work and ensures friendly and non-oppressive preventive care environments for sex workers. [ABSTRACT FROM AUTHOR]

Objective: To assess the effectiveness of different machine learning models in estimating the pharmaceutical and non-pharmaceutical expenditures associated with Diabetes Mellitus type II diagnosis, based on the clinical risk index determined by the analysis of comorbidities. Materials and methods: In this cross-sectional study, we have used data from 11,028 anonymized records of patients admitted to a high-complexity hospital in Bogota, Colombia between 2017–2019 with a primary diagnosis of Diabetes. These cases were classified according to Charlson's comorbidity index in several risk categories. The main variables analyzed in this study are hospitalization costs (which include pharmaceutical and non-pharmaceutical expenditures), age, gender, length of stay, medicines and services consumed, and comorbidities assessed by the Charlson's index. The model's dependent variable is expenditure (composed of pharmaceutical and non-pharmaceutical expenditures). Based on these variables, different machine learning models (Multivariate linear regression, Lasso model, and Neural Networks) were used to estimate the pharmaceutical and non-pharmaceutical expenditures associated with the clinical risk classification. To evaluate the performance of these models, different metrics were used: Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Determination (R2). Results: The results indicate that the Neural Networks model performed better in terms of accuracy in predicting pharmaceutical and non-pharmaceutical expenditures considering the clinical risk based on Charlson's comorbidity index. A deeper understanding and experimentation with Neural Networks can improve these preliminary results, therefore we can also conclude that the main variables used and those that were proposed can be used as predictors for the medical expenditures of patients with diabetes type-II. Conclusions: With the increase of technology elements and tools, it is possible to build models that allow decision-makers in hospitals to improve the resource planning process given the accuracy obtained with the different models tested. [ABSTRACT FROM AUTHOR]

Copyright of Studia Universitatis Babes-Bolyai, Psychologia-Paedagogia is the property of Babes-Bolyai University, Cluj-Napoca, Romania and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: Traffic-related crashes are a leading cause of premature death and disability. The safe systems approach is an evidence-informed set of innovations to reduce traffic-related injuries and deaths. First developed in Sweden, global health actors are adapting the model to improve road safety in low- and middle-income countries via technical assistance (TA) programs; however, there is little evidence on road safety TA across contexts. This study investigated how, why, and under what conditions technical assistance influenced evidence-informed road safety in Accra (Ghana), Bogotá (Colombia), and Mumbai (India), using a case study of the Bloomberg Philanthropies Initiative for Global Road Safety (BIGRS). Methods: We conducted a realist evaluation with a multiple case study design to construct a program theory. Key informant interviews were conducted with 68 government officials, program staff, and other stakeholders. Documents were utilized to trace the evolution of the program. We used a retroductive analysis approach, drawing on the diffusion of innovation theory and guided by the context-mechanism-outcome approach to realist evaluation. Results: TA can improve road safety capabilities and increase the uptake of evidence-informed interventions. Hands-on capacity building tailored to specific implementation needs improved implementers' understanding of new approaches. BIGRS generated novel, city-specific analytics that shifted the focus toward vulnerable road users. BIGRS and city officials launched pilots that brought evidence-informed approaches. This built confidence by demonstrating successful implementation and allowing government officials to gauge public perception. But pilots had to scale within existing city and national contexts. City champions, governance structures, existing political prioritization, and socio-cultural norms influenced scale-up. Conclusion: The program theory emphasizes the interaction of trust, credibility, champions and their authority, governance structures, political prioritization, and the implement-ability of international evidence in creating the conditions for road safety change. BIGRS continues to be a vehicle for improving road safety at scale and developing coalitions that assist governments in fulfilling their role as stewards of population well-being. Our findings improve understanding of the complex role of TA in translating evidence-informed interventions to country-level implementation and emphasize the importance of context-sensitive TA to increase impact. [ABSTRACT FROM AUTHOR]

Radon (222 Rn) is a naturally occurring gas that represents a health threat due to its causal relationship with lung cancer. Despite its potential health impacts, several regions have not conducted studies, mainly due to data scarcity and/or economic constraints. This study aims to bridge the baseline information gap by building an interactive dashboard (http://ircmodelingdashboard.eu.pythonanywhere.com/ , last access: 17 April 2024) that uses inferential statistical methods to estimate the spatial distribution of indoor radon concentration (IRC) for a target area. We demonstrate the functionality of the dashboard by modeling IRC in the city of Bogotá, Colombia, using 30 in situ measurements. IRC measured was the highest reported in the country, with a geometric mean of 91±14 Bq m -3 and a maximum concentration of 407 Bq m -3. In 57 % of the residences, RC exceeded the WHO's recommendation of 100 Bq m -3. A prediction map for houses registered in Bogotá's cadaster was built in the dashboard by using a log-linear regression model fitted with the in situ measurements, together with meteorological, geologic and building-specific variables. The model showed a cross-validation root mean squared error of 57 Bq m -3. Furthermore, the model showed that the age of the house presented a statistically significant positive association with RC. According to the model, IRC measured in houses built before 1980 presents a statistically significant increase of 72 % compared to IRC of those built after 1980 (p value = 0.045). The prediction map exhibited higher IRC in older buildings most likely related to cracks in the structure that could enhance gas migration in older houses. This study highlights the importance of expanding 222 Rn studies in countries with a lack of baseline values and provides a cost-effective alternative that could help deal with the scarcity of IRC data and get a better understanding of place-specific variables that affect IRC spatial distribution. [ABSTRACT FROM AUTHOR]

Introduction: Nature-based solutions are increasingly recognized as vital components of urban resilience strategies, particularly within the framework of green infrastructure. This study aims to propose an approach that fosters symbiosis between green and gray infrastructure to address the challenges posed by climate change in urban environments. Methods: We conducted a comprehensive review of guidelines and scientific literature to inform the selection of species and the design of root containers for urban tree planting. Additionally, we performed a multicriteria analysis and assessed water comfort to guide decision-making regarding species selection in specific city areas. Results: The methodology was applied to a case study in Bogotá, yielding insights applicable to any city with basic knowledge of suitable species for planting in built public spaces. Crucial criteria for selecting local species for sidewalks were identified, including size, permeability, soil compaction characteristics, and climatic adaptability. A list of desirable species adapted to all humidity zones of the case study city was generated. Hydrological sizing methods proposed are contingent upon both the species to be planted and the geometry of the streets. Discussion: The approach and findings presented in this study promote the development of trees and their ecosystem services while mitigating potential damage to surrounding infrastructure. Conclusion: Implementing strategies that facilitate symbiosis between green and gray infrastructure contributes to urban resilience and aids in climate change adaptation efforts. [ABSTRACT FROM AUTHOR]

Bidens pilosa L., native to South America and commonly used for medicinal purposes, has been understudied at molecular and genomic levels and in its relationship with soil microorganisms. In this study, restriction site-associated DNA markers (RADseq) techniques were implemented to analyze genetic diversity and population structure, and metabarcoding to examine microbial composition in soils from Palmira, Sibundoy, and Bogotá, Colombia. A total of 2,984,123 loci and 3485 single nucleotide polymorphisms (SNPs) were identified, revealing a genetic variation of 12% between populations and 88% within individuals, and distributing the population into three main genetic groups, FST = 0.115 (p < 0.001) and FIT = 0.013 (p > 0.05). In the soil analysis, significant correlations were found between effective cation exchange capacity (ECEC) and apparent density, soil texture, and levels of Mg and Fe, as well as negative correlations between ECEC and Mg, and Mg, Fe, and Ca. Proteobacteria and Ascomycota emerged as the predominant bacterial and fungal phyla, respectively. Analyses of alpha, beta, and multifactorial diversity highlight the influence of ecological and environmental factors on these microbial communities, revealing specific patterns of clustering and association between bacteria and fungi in the studied locations. [ABSTRACT FROM AUTHOR]

Copyright of Revista de Economía Institucional is the property of Universidad Externado de Colombia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)