La enseñanza de las ciencias en la población estudiantil es un factor clave para que un país cuente con una masa crítica de ciudadanos, alfabetizada científicamente, que facilite y promueva el desarrollo tecnológico, económico y social de una nación, bajo un enfoque sustentable. Este artículo se orienta a presentar los detalles de la implementación de la propuesta de enseñanza de las ciencias basada en la indagación (ECBI), propuesta que se está posicionando internacionalmente como herramienta efectiva, para el desarrollo de competencias científicas, tecnológicas y ciudadanas en los estudiantes, en un particular contexto latinoamericano: Programa Pequeños Científicos (PPC) en la región Tolima-Colombia. Los resultados evidencian la importancia, para el éxito del Programa en esta región, de la presencia de factores exógenos al aula de clase como el contexto institucional, así como la presencia de factores endógenos vinculados al docente, y al estudiante. Asímismo los principales hallazgos dan cuenta de la apropiación de una metodología por parte de los docentes y de cambios en ambiente de aprendizaje de las clases de ciencias.

This paper presents a humanitarian aid distribution framework designed to address accessibility limitations caused by temporary road disruptions. The proposed mathematical programming model minimizes total arrival time at delivery points while considering route continuity, vehicle scheduling, fleet capacity, and road repair progress. Validation was performed using a real-world case study, and scenario analyses were conducted to assess the impact of variations in fleet availability, road damage ratios, and repair times on total arrival time. The results revealed that synchronizing the departure and arrival times of vehicles at each destination node based on the progress of road repairs can minimize the time required for aid vehicles to reach the destination nodes, offering practical applications for improving logistical processes. This approach contributed to understanding the impact of road disruptions and the progress of road restoration activities on the configuration of humanitarian aid distribution routes and the overall operation time. As an additional contribution, a methodological approach was considered for establishing the arrival and departure times of a limited number of vehicles to demand points, taking into account the expected completion times of repair operations on affected roads.

The paper examines a proposal on scheduling repair resources to deal with temporary road disruptions in humanitarian aid networks. A mathematical model was formulated, which took minimizing the total time of completion of the repair, as well as the arrival and departure times of crews and repair teams, and relations of precedence and complementarity between resources and the availability of remedies into consideration. To validate the model, a real case study was used, where a region is presented, which has been affected by floods that generate temporary road disruptions. Finally, a scenario analysis of the model was performed so that the impact on performance related to the variation of parameters of interest, such as the availability of resources, the repair times of the crews, the machine operating times and the expected restoration completion time can be studied. The results showed that the repair resource scheduling process can take into account the constraints of precedence and complementarity between resources as a consequence of the temporary road repair. This also shows that the impact on the scheduling of repair resources, and the total time of repair is based on the required characteristics or conditions of the roads that are going to be repaired. This research is a contribution on the importance of relations of precedence in the scheduling for road repair, the interdependence of resources and the special conditions of allocation between crews and type of machinery according to the affected track, and its impact on the completion times of the repair.

Over the past few decades, the study of leadership theory has expanded across various disciplines, delving into the intricacies of human behavior and defining the roles of individuals within organizations. Its primary objective is to identify leaders who play significant roles in the communication flow. In addition, behavioral theory posits that leaders can be distinguished based on their daily conduct, while social network analysis provides valuable insights into behavioral patterns. Our study investigates five and six types of social networks frequently observed in different organizations. This study is conducted using datasets we collected from an IT company and public datasets collected from a manufacturing company for the thorough evaluation of prediction performance. We leverage PageRank and effective word embedding techniques to obtain novel features. State-of-the-art performance is obtained using various statistical machine learning methods, graph convolutional networks (GCN), automated machine learning (AutoML), and explainable artificial intelligence (XAI). More specifically, our approach can achieve state-of-the-art performance with an accuracy close to 90% for leaders identification with data from projects of different types. This investigation contributes to the establishment of sustainable leadership practices by aiding organizations in retaining their leadership talent.

BACKGROUND:Transportation policies can impact health outcomes while simultaneously promoting social equity and environmental sustainability. We developed an agent-based model (ABM) to simulate the impacts of fare subsidies and congestion taxes on commuter decision-making and travel patterns. We report effects on mode share, travel time and transport-related physical activity (PA), including the variability of effects by socioeconomic strata (SES), and the trade-offs that may need to be considered in the implementation of these policies in a context with high levels of necessity-based physical activity. METHODS:The ABM design was informed by local stakeholder engagement. The demographic and spatial characteristics of the in-silico city, and its residents, were informed by local surveys and empirical studies. We used ridership and travel time data from the 2019 Bogotá Household Travel Survey to calibrate and validate the model by SES. We then explored the impacts of fare subsidy and congestion tax policy scenarios. RESULTS:Our model reproduced commuting patterns observed in Bogotá, including substantial necessity-based walking for transportation. At the city-level, congestion taxes fractionally reduced car use, including among mid-to-high SES groups but not among low SES commuters. Neither travel times nor physical activity levels were impacted at the city level or by SES. Comparatively, fare subsidies promoted city-level public transportation (PT) ridership, particularly under a 'free-fare' scenario, largely through reductions in walking trips. 'Free fare' policies also led to a large reduction in very long walking times and an overall reduction in the commuting-based attainment of physical activity guidelines. Differential effects were observed by SES, with free fares promoting PT ridership primarily among low-and-middle SES groups. These shifts to PT reduced median walking times among all SES groups, particularly low-SES groups. Moreover, the proportion of low-to-mid SES commuters meeting weekly physical activity recommendations decreased under the 'freefare' policy, with no change observed among high-SES groups. CONCLUSIONS:Transport policies can differentially impact SES-level disparities in necessity-based walking and travel times. Understanding these impacts is critical in shaping transportation policies that balance the dual aims of reducing SES-level disparities in travel time (and time poverty) and the promotion of choice-based physical activity.

Purpose: This paper aims to enrich understanding of the obesity transition among socioeconomic status (SES) strata by gender and age in cities of Colombia and Mexico. The study uses harmonized data from the Salud Urbana en América Latina (SALURBAL) study. Methods: A population-level system dynamics model was developed using 2010 and 2015 data from Colombia and 2012 and 2016 data from Mexico (national health surveys). The model simulates the prevalence of different BMI categories (i.e., not overweight, overweight, obese) stratified by gender, age, and SES, in the SALURBAL cities (aggregated to the country level) of Colombia and Mexico from 2010 to 2050. Sample sizes for Colombia in 2010 and Mexico in 2012 were 7420 and 5785 children (<5 years), 21601 and 14413 children and adolescents (5–17 years), and 46597 and 20464 adults (18–64 years), respectively. Sample sizes for Colombia in 2015 and Mexico in 2016 were 4450 and 907 children, 12468 and 2350 children and adolescents, and 90430 and 3413 adults, respectively. Results: For men in Colombia and Mexico, the burden of obesity is projected to increase among lower SES adults over time. Colombian women show similar patterns observed in men but the burden of obesity was already greater in the lower SES groups as early as 2012. In Mexican women, the burden of obesity in 2012 is higher in the lower SES population; however, the prevalence of obesity is projected to increase at a faster rate in the higher SES population. Patterns for children aged 0–14 years differed by gender and country. Conclusions: The model suggests that the prevalence of obesity among SES strata by age and gender in SALURBAL cities of Colombia and Mexico are likely to change over time, and predicts their possible evolution through the different stages of the obesity transition.

The assessment of urban transport interventions is complex, multi-faceted, and context dependent. This study proposes a multi-methodology approach called systems analytics to evaluate the potential impact of the implementation of temporary bike paths during the COVID-19 pandemic on Bogota's bicycle complex system. The proposed methodology applies systems theory to identify the complexity, barriers, and facilitators of the system and uses statistical and simulation methods to assess the potential impact of temporary bike paths on the safety and quality of life of bicycle users in Bogota during the COVID-19 pandemic. The results of the case study indicate that the temporary bike paths could have been a factor that helped reduce bicycle collision rates (by 56%), increased the use of street segments classified with low levels of traffic stress (by 6.22%), and prevented premature deaths (145 per year). The proposed methodology is helpful for policymakers who aim to design active transport interventions in support of a sustainable and healthy environment.

Purpose The study aims to present an agent-based simulation model (ABM) for exploring interorganizational coordination scenarios in local disaster preparedness. This approach includes local actors and logistical processes as agents to compare various strategic coordination mechanisms. Design/methodology/approach The ABM model, developed in the Latin American context, specifically focuses on a case study of Colombia. Three coordination mechanisms (centralized, decentralized and cluster-type) have been evaluated using three performance indicators: effectiveness, efficiency and flexibility. Findings Simulation results show that the decentralized scenario outperforms in terms of efficiency and flexibility. On the contrary, the centralized and cluster-type scenarios demonstrate higher effectiveness, achieving a greater percentage of requirements coverage during the disaster preparedness stage. The ABM approach effectively evaluates strategical coordination mechanisms based on the analyzed performance indicators. Research limitations/implications This study has limitations due to the application of results to a single real case. In addition, the focus of the study is primarily on a specific type of disaster, specifically hydrometeorological events such as flash floods, torrential rains and landslides. Moreover, the scope of decision-making is restricted to key actors involved in local-level disaster management within a municipality. Originality/value The proposed ABM model has the potential as a decision-making tool for policies and local coordination schemes for future disasters. The simulation tool could also explore diverse geographical scenarios and disaster types, demonstrating its versatility and broader applicability for further insights and recommendations.

Purpose The purpose of this paper is to review the relevant literature in order to identify trends and suggest some possible directions for future research in the framework of humanitarian aid distribution logistics with accessibility constraints. Design/methodology/approach The authors developed a systematic literature review to study the state of the art on distribution logistics considering accessibility constraints. The electronic databases used were Web of science, Scopus, Science Direct, Jstor, Emerald, EBSCO, Scielo and Redalyc. As a result, 49 articles were reviewed in detail. Findings This study identified some gaps, as well as some research opportunities. The main conclusions are the need for further studies on the interrelationships and hierarchies of multiple actors, explore intermodality, transshipment options and redistribution relief goods to avoid severe shortages in some nodes and excess inventory in others, studies of the vulnerability of transport networks, correlational analysis of road failures and other future lines. Research limitations/implications The bibliography is limited to peer-reviewed academic journals due to their academic relevance, accessibility and ease of searching. Most of the studies included in the review were conducted in high-income countries, which may limit the generalizability of the results to low-income countries. However, the authors focused on databases covering important journals on humanitarian logistics. Originality/value This paper contextualises and synthesises research into humanitarian aid distribution logistics with accessibility constrains, highlights key themes and suggests areas for further research.

Purpose The aim of this study is to increase the understanding of collaborative relationships and assess according to the project size, the influence of the contributory factors in shaping collaboration network structure in projects developed in global supply chains (GSC). Design/methodology/approach The paper used a case study methodology applied to eight global projects developed by an Austrian company leader in global market intra-logistics solutions and warehouse automation. The cases were studied by two approaches in network analysis. First, visual and descriptive analysis to describe structural aspects of the network. Second, stochastic network analysis to evaluate the influence of contributory factors in the structure of the collaboration network. Findings The results evidence that independently of the project size and project manager influence, project team roles (PTR) who have a reciprocal communication among other PTR tend to have a higher collaboration intensity (CI). Additionally, the results highlight the influence of the project manager in shaping the collaboration network in standard projects (STP) and small projects (SMP). According to the project size, the results show that the PTR that form complete triangles or cluster or who communicate frequently among each other tend to have a high CI, being more evident these tendencies in large-scale projects than STP and SMP. Originality/value This research provides a framework to identify the key actors and contributory factors in shaping collaborative relationships in GSC. The findings could be used to support the decision-making process and formulation strategies for effective collaborative relationship management in GSC.

OBJECTIVE:We investigate the obesity transition at the country- and regional-levels, by age, gender, and socioeconomic status (SES) and its relationship to three health behavior attributes, including physical activity (PA), sedentary activities (ST), and consumption of ultra-processed foods (CUPF) within the urban population of Colombia, from 20,010 to 2050. METHODS:The study is informed by cross-sectional data from ENSIN survey. We used these data to develop a system dynamics model that simulates the dynamics of obesity by body mass index (BMI) categories, gender, and SES. This model also uses a conservative co-flow structure for three health-related behaviors (PA, ST, and CUPF). RESULTS:At the national level, our results indicate that the burden of obesity is shifting towards populations with lower SES as the gross domestic product (GDP) increases, particularly women aged 20-59 years with lower SES. Among this group of women, the highest burden of obesity is among those who do not meet the PA, ST and CUPF recommendations. At the regional level, our findings suggest that the regions are at different stages in the obesity transition. CONCLUSIONS:The burden of obesity is shifting towards women with lower SES as GDP increases at the national level and across several regions. This obesity transition is paralleled by a high prevalence of women from low SES groups who do not meet the minimum recommendations for PA, CUPF, and ST. Our findings can be used by decision-makers to inform age- and SES- specific policies seeking to tackle the obesity.

Purpose In Latin America and the Caribbean, the access of students to higher education has presented an extraordinary growth over the past fifteen years. This rapid growth has presented a challenge for increasing the system resources and capabilities while maintaining its quality. As a result, the networked universities (NUs) organized themselves as a collaborative network, and they have become an interesting model for facing the complexity driven by globalization, rapidly changing technology, dynamic growth of knowledge and highly specialized areas of expertise. In this article, we studied the NU named Red Universitaria Mutis (Red Mutis) with the aim of characterizing the collaboration and integration structure of the network. Design/methodology/approach Network analytic methods (visual analysis, positional analysis and a stochastic network method) were used to characterize the organizational structure and robustness of the network, and to identify what variables or structural tendencies are related to the likelihood that specific areas of a university would collaborate. Findings Red Mutis is a good example of regional NUs that could take advantage of the strengths, partnerships, information and knowledge of the regional and international universities that form the network. Analyses showed that Red Mutis has a differentiated structure consisting of academic and non-academic university areas with a vertical coordination (by steering and management) of the different university areas. Originality/value The methodology could be used as a framework to analyze and strengthen other strategic alliances between universities and as a model for the development of other NU in local and global contexts.

Urban health is shaped by a system of factors spanning multiple levels and scales, and through a complex set of interactions. Building on causal loop diagrams developed via several group model building workshops, we apply the cross-impact balance (CIB) method to understand the strength and nature of the relationships between factors in the food and transportation system, and to identify possible future urban health scenarios (i.e., permutations of factor states that impact health in cities). We recruited 16 food and transportation system experts spanning private, academic, non-government, and policy sectors from six Latin American countries to complete an interviewer-assisted questionnaire. The questionnaire, which was pilot tested on six researchers, used a combination of questions and visual prompts to elicit participants' perceptions about the bivariate relationships between 11 factors in the food and transportation system. Each participant answered questions related to a unique set of relationships within their domain of expertise. Using CIB analysis, we identified 21 plausible future scenarios for the system. In the baseline model, 'healthy' scenarios (with low chronic disease, high physical activity, and low consumption of highly processed foods) were characterized by high public transportation subsidies, low car use, high street safety, and high free time, illustrating the links between transportation, free time and dietary behaviors. In analyses of interventions, low car use, high public transport subsidies and high free time were associated with the highest proportion of factors in a healthful state and with high proportions of 'healthy' scenarios. High political will for social change also emerged as critically important in promoting healthy systems and urban health outcomes. The CIB method can play a novel role in augmenting understandings of complex urban systems by enabling insights into future scenarios that can be used alongside other approaches to guide urban health policy planning and action.

We study the relevance of considering social network analysis in determining soccer results. As a benchmark, we start using a simple regression model based on past performance to try to determine the main trends of a soccer match based on probabilities of winning, losing or tying, as home or visiting teams. The success of this simple model, based on historical performance, is improved by the addition of network descriptors of both teams in a game. Therefore, such network measures do offer additional useful information in determining match outcomes. We validate our approach using the data of the Spanish League (La Liga) 2012–2013. We observe that betweenness centrality seems to provide additional relevance information related to the performance of a team during the tournament.

Behavioral theory assumes that leaders can be identified by their daily behaviors. Social network analysis helps to understand behavioral patterns within their social networks. This work considers leaders as the managerial personnel of the organization and differentiates managements from non-managerial staff by their behavior with five different types of interactions with PageRank and their attributes in modern organizations. PageRank and word embedding using word2vec with phrases from features are adopted to extract new features for the identification of managerial staff. Both traditional machine learning methods and graph neural networks are utilized with real-world data from an Austrian IT company called Knapp System Integration. Our experimental results show that the proposed new features extracted using PageRank with different types of interactions and word2vec with phrases significantly improve the identification accuracy. We also propose to use graph neural networks as an effective learning algorithm to identify managers from organizations. Our approach can identify managerial staff with an accuracy of around 80%, which demonstrates that managers could be identified through social network analysis. By analyzing the behaviors of members, the proposed method is effective as a performance appraisal tool for organizations. The study facilitates sustainable management by helping organizations to retain managerial talents or to invite potential talents to join the management team.

Cross-impact balance (CIB) analysis leverages expert knowledge pertaining to the nature and strength of relationships between components of a system to identify the most plausible future ‘scenarios’ of the system. These scenarios, also referred to as ‘storylines’, provide qualitative insights into how the state of one factor can either promote or restrict the future state of one or multiple other factors in the system. This paper presents a novel, visually oriented questionnaire developed to elicit expert knowledge about the relationships between key factors in a system, for the purpose of CIB analysis. The questionnaire requires experts to make selections from a series of standardized cause-effect graphical profiles that depict a range of linear and non-linear relationships between factor pairs. The questionnaire and the process of translating the graphical selections into data that can be used for CIB analysis is described using an applied example which focuses on urban health in Latin American cities.•A questionnaire featuring a set of standardized cause-effect profiles was developed.•Cause-effect profiles were used to elicit information about the strength of linear and non-linear bivariate relationships.•The questionnaire represents an intuitive visual means for collecting data required for the conduct of CIB analysis.

Urban transportation is an important determinant of health and environmental outcomes, and therefore essential to achieving the United Nation's Sustainable Development Goals. To better understand the health impacts of transportation initiatives, we conducted a systematic review of longitudinal health evaluations involving: a) bus rapid transit (BRT); b) bicycle lanes; c) Open Streets programs; and d) aerial trams/cable cars. We also synthesized systems-based simulation studies of the health-related consequences of walking, bicycling, aerial tram, bus and BRT use. Two reviewers screened 3302 unique titles and abstracts identified through a systematic search of MEDLINE (Ovid), Scopus, TRID and LILACS databases. We included 39 studies: 29 longitudinal evaluations and 10 simulation studies. Five studies focused on low- and middle-income contexts. Of the 29 evaluation studies, 19 focused on single component bicycle lane interventions; the rest evaluated multi-component interventions involving: bicycle lanes (n = 5), aerial trams (n = 1), and combined bicycle lane/BRT systems (n = 4). Bicycle lanes and BRT systems appeared effective at increasing bicycle and BRT mode share, active transport duration, and number of trips using these modes. Of the 10 simulation studies, there were 9 agent-based models and one system dynamics model. Five studies focused on bus/BRT expansions and incentives, three on interventions for active travel, and the rest investigated combinations of public transport and active travel policies. Synergistic effects were observed when multiple policies were implemented, with several studies showing that sizable interventions are required to significantly shift travel mode choices. Our review indicates that bicycle lanes and BRT systems represent promising initiatives for promoting population health. There is also evidence to suggest that synergistic effects might be achieved through the combined implementation of multiple transportation policies. However, more rigorous evaluation and simulation studies focusing on low- and middle-income countries, aerial trams and Open Streets programs, and a more diverse set of health and health equity outcomes is required.

Walking and biking to school represent a source of regular daily physical activity (PA). The objectives of this paper are to determine the associations of distance to school, crime safety, and socioeconomic variables with active school transport (AST) among children from five culturally and socioeconomically different country sites and to describe the main policies related to AST in those country sites. The analytical sample included 2845 children aged 9-11 years from the International Study of Childhood Obesity, Lifestyle and the Environment. Multilevel generalized linear mixed models were used to estimate the associations between distance, safety and socioeconomic variables, and the odds of engaging in AST. Greater distance to school and vehicle ownership were associated with a lower likelihood of engaging in AST in sites in upper-middle- and high-income countries. Crime perception was negatively associated to AST only in sites in high-income countries. Our results suggest that distance to school is a consistent correlate of AST in different contexts. Our findings regarding crime perception support a need vs. choice framework, indicating that AST may be the only commuting choice for many children from the study sites in upper-middle-income countries, despite the high perception of crime.

The explosion of network science has permitted an understanding of how the structure of social networks affects the dynamics of social contagion. In community-based interventions with spill-over effects, identifying influential spreaders may be harnessed to increase the spreading efficiency of social contagion, in terms of time needed to spread all the largest connected component of the network. Several strategies have been proved to be efficient using only data and simulation-based models in specific network topologies without a consensus of an overall result. Hence, the purpose of this paper is to benchmark the spreading efficiency of seeding strategies related to network structural properties and sizes. We simulate spreading processes on empirical and simulated social networks within a wide range of densities, clustering coefficients, and sizes. We also propose three new decentralized seeding strategies that are structurally different from well-known strategies: community hubs, ambassadors, and random hubs. We observe that the efficiency ranking of strategies varies with the network structure. In general, for sparse networks with community structure, decentralized influencers are suitable for increasing the spreading efficiency. By contrast, when the networks are denser, centralized influencers outperform. These results provide a framework for selecting efficient strategies according to different contexts in which social networks emerge.

Background: Cable cars provide urban mobility benefits for vulnerable populations. However, no evaluation has assessed cable cars' impact from a health perspective. TransMiCable in Bogotá, Colombia, provides a unique opportunity to (1) assess the effects of its implementation on the environmental and social determinants of health (microenvironment pollution, transport accessibility, physical environment, employment, social capital, and leisure time), physical activity, and health outcomes (health-related quality of life, respiratory diseases, and homicides); and (2) use citizen science methods to identify, prioritize, and communicate the most salient negative and positive features impacting health and quality of life in TransMiCable's area, as well as facilitate a consensus and advocacy-building change process among community members, policymakers, and academic researchers.Methods: TrUST (In Spanish: Transformaciones Urbanas y Salud: el caso de TransMiCable en Bogotá) is a quasi-experimental study using a mixed-methods approach. The intervention group includes adults from Ciudad Bolívar, the area of influence of TransMiCable. The control group includes adults from San Cristóbal, an area of future expansion for TransMiCable. A conceptual framework was developed through group-model building. Outcomes related to environmental and social determinants of health as well as health outcomes are assessed using questionnaires (health outcomes, physical activity, and perceptions), secondary data (crime and respiratory outcomes) use of portable devices (air pollution exposure and accelerometry), mobility tracking apps (for transport trajectories), and direct observation (parks). The Stanford Healthy Neighborhood Discovery Tool is being used to capture residents' perceptions of their physical and social environments as part of the citizen science component of the investigation.Discussion: TrUST is innovative in its use of a mixed-methods, and interdisciplinary research approach, and in its systematic engagement of citizens and policymakers throughout the design and evaluation process. This study will help to understand better how to maximize health benefits and minimize unintended negative consequences of TransMiCable.