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The deterioration of the environment and especially the crisis due to the availability of water, has led developing countries to advance in protection, avoiding the dumping of wastewater and monitoring quality. In the city of Medellín, the use of electrical conductivity as an indicator of water quality in the Aburrá-Medellín river was proposed as an alternative to explain and keep the community informed about how the river monitoring stations are doing through of colors and thus continue to raise awareness of the importance of caring for water. In order to study the spatial and temporal variations of the water quality in the river from the electrical conductivity indicator, and considering the categorical data obtained with the indicator, a number from 1 to 5 was assigned (very bad, bad, regular, acceptable and good quality, respectively). In these paper We used statistical methods as crosstabulation to describe relationships between the categorical variables through counts and multiple correspondence analysis for representing the associations between the factors affecting the wáter quality using the correspondence maps. It was found that low flows correspond to the most critical quality conditions, and although a deterioration is observed as the river flows downstream (between the monitoring stations) the influence of the water levels is greater. The statistical analysis showed a relatively high association of the quality indicator and the flows also, that the water quality deteriorates in the afternoon and in the final years of monitoring in low flows. [ABSTRACT FROM AUTHOR]

This paper explores the scope for upscaling and transnational transfer of participatory landslide risk management strategies for informal settlements in Latin America. Drawing on lessons from transdisciplinary action-research in three neighbourhoods in Medellín, Colombia, and one in São Paulo, Brazil, the paper discusses how bottom-up approaches were developed to co-produce landslide risk management in both Global South cities, in a way that optimises the collaboration between communities and relevant governmental bodies. The analysis focuses on mitigation and 'agreement-seeking', from the perspectives of scale, power and knowledge, which help understand the parallels between co-production of landslide risk management and co-production of urban services. Two key conclusions are that landslide risk management should be built into neighbourhood upgrading and management, and that both community and the state have stronger roles to play in landslide risk management from their respective capacities. The paper also reflects on the role of academia in enabling co-production of landslide risk management through engaging with local communities. [ABSTRACT FROM AUTHOR]

This paper presents a methodology to estimate the multidimensional poverty index using spatial data at the street block level. The data used in this study were obtained from Open Street Maps and ESA's land use cover, which are freely available sources of spatial information. The study employs five machine-learning algorithms, including Catboost, Lightboost, and Random Forest, to estimate the multidimensional poverty index with spatial granularity. The results indicate that these models achieve promising performance in predicting poverty levels in Medellín, Colombia. The results showed that the Random Forest algorithm achieved the highest performance, with an MAE of 0.07504. Furthermore, the spatial distribution of the multidimensional poverty estimate was highly correlated with the true values of the distribution. This work contributes to predicting multidimensional poverty by demonstrating the potential of machine learning algorithms to utilize accessible spatial data. By providing evidence of the feasibility of estimating poverty levels at a granular spatial level, this methodology offers a powerful tool for policymakers to make poverty social interventions with low-cost evidence. Furthermore, this study has important implications for poverty eradication efforts in developing countries, where access to reliable data remains challenging. [ABSTRACT FROM AUTHOR]

This paper deals with the problem regarding the optimal operation of photovoltaic (PV) generation sources in AC distribution networks with a single-phase structure, taking into consideration different objective functions. The problem is formulated as a multi-period optimal power flow applied to AC distribution grids, which generates a nonlinear programming (NLP) model with a non-convex structure. Three different objective functions are considered in the optimization model, each optimized using a single-objective function approach. These objective functions are (i) an operating costs function composed of the energy purchasing costs at the substation bus, added with the PV maintenance costs; (ii) the costs of energy losses; and (iii) the total CO2 emissions at the substation bus. All these functions are minimized while considering a frame of operation of 24 h, i.e., in a day-ahead operation environment. To solve the NLP model representing the studied problem, the General Algebraic Modeling System (GAMS) and its SNOPT solver are used. Two different test feeders are used for all the numerical validations, one of them adapted to the urban operation characteristics in the Metropolitan Area of Medellín, which is composed of 33 nodes, and the other one adapted to isolated rural operating conditions, which has 27 nodes and is located in the department of Chocó, Colombia (municipality of Capurganá). Numerical comparisons with multiple combinatorial optimization methods (particle swarm optimization, the continuous genetic algorithm, the Vortex Search algorithm, and the Ant Lion Optimizer) demonstrate the effectiveness of the GAMS software to reach the optimal day-ahead dispatch of all the PV sources in both distribution grids. [ABSTRACT FROM AUTHOR]

This paper concerns the numerical performance evaluation of multi-degree-of-freedom systems equipped with Tuned Mass Dampers-Inerter (TMDIs); a passive control device used for the mitigation of mechanical vibrations induced by dynamic loads. The inerter device is commonly used to increase the apparent mass of classics tuned mass dampers (TMDs), improving its seismic performance. To evaluate the TMDI action, three case studies are employed, determined from three real buildings of Medellin city from low, medium to high rise (30 meters, 97 meters, and 144 meters, respectively). Optimum design parameters are found using a metaheuristic optimization based on the differential evolution method, first, for the minimization of the horizontal peak displacements, and then, for the minimization of the root mean square (RMS) response of displacements. Besides, the case studies are assessed using eight seismic accelerations records representative of the literature. Finally, the seismic performance is evaluated on each case study considering different levels of inertance induced by the inerter device: 5%, 20%, and 50% with respect to the total mass of the building, for which it is observed a better dynamic behavior when TMDIs with lower values of inertance are implemented. [ABSTRACT FROM AUTHOR]

This paper analyzes the amount of truck emissions and their variations according to changes in travel schedules or routes and the impact on human health represented by cases of Acute Respiratory Infections (ARI) due to PM2.5 emissions in Medellín, Colombia. To accomplish this, information on each vehicle was collected, including model, year, type of fuel used, Euro, and engine power trucks. The commercial vehicles were equipped with GPS to obtain second-to-second speed, location, acceleration, and deceleration; the rest of the data were provided by the vehicles' owners. All this information was used to estimate emissions with the HBEFA model. The main findings show a decrease of approximately 38% in emissions by changing the truck circulation schedule to off-hours and a generation of 2.35 annual cases of ARI if the amount of PM2.5 increases 1 μg/m3. Moreover, this investigation proposes that the optimal inter-city speed for truck circulation is between 40 km/h and 50 km/h, and it is recommended that some cargo transport operations should be carried out during off-hours, especially at night. [ABSTRACT FROM AUTHOR]

Objective: To validate a Food Diversity Questionnaire (CDA, for its name in Spanish) that identifies the prevalence of the risk of deficiency in the intake of eleven micronutrients. Design: The CDA paper form, an online application for data entry and handling, was designed and compared with the 24-h recall (24HR) as a reference method. All data were processed in Personal Computer Software for Intake Distribution Estimation (PC-SIDE) v1 software. A descriptive analysis and comparisons between prevalence, concordance and reproducibility analyses were performed. Setting: Medellín, Colombia. Participants: Women of childbearing age between 19 and 50 years (n 186) who worked for the Buen Comienzo programme in 2019. Results: When comparing the adjusted 24HR technique and the CDA, there was no significant difference in population-level data at risk of deficiency in any micronutrient intake. However, based on individual-level data of the best linear unbiased predictor, the concordance analyses were weak, and although agreements were high according to the diagnostic performance tests, a good ability to detect deficiency was only observed in a few nutrients: vitamin A 100·0 %, Ca 98·7 %, Fe 92·8 %, folates 91·6 %, and pyridoxine 81·8 %. Conclusions: The CDA validated in this study is useful and faster at evaluating population-level data at risk of deficiency in the intake of Ca, Fe, Zn, thiamine, riboflavin, niacin, pyridoxine, folates, vitamin B12, vitamin C and vitamin A. Based on individual-level data, a good ability to detect deficiencies was observed in the intake of vitamin A, Ca, Fe, folates and pyridoxine. [ABSTRACT FROM AUTHOR]

This paper deals with the optimal sizing of islanded microgrids (MGs), which use diesel generators to supply energy in off-grid areas. The MG under study integrates photovoltaic (PV) and diesel generation, a battery energy storage System (BESS), and an inverter for the connection between AC and DC voltage buses. Levelised cost of energy (LCOE) and annual system cost (ASC) are considered economic indicators, while the loss of power supply probability (LPSP) is used as a reliability indicator. Fiscal incentives such as the tax benefits and accelerated depreciation applied in Colombia are considered for the optimally sizing of each MG element. Solar measurements were taken at a weather station located in the main campus of Universidad de Antioquia in Medellin, Colombia at a latitude of 6.10 and longitude of −75.38. The objective function is the minimization of the total energy delivered from the power sources that successfully meets the load. The model was implemented in Python programming language considering several scenarios. Two cases were evaluated: the first one considered PV panels, a BESS and a diesel generator, while the second one only considered PV panels and a BESS. The option that does not include the diesel generator turned out to be the most expensive, since additional PV and BESS resources are required to meet the load profile. Furthermore, it was found that the LCOE was lower when tax benefits were taken into account. [ABSTRACT FROM AUTHOR]

Pigs subjected to thermal conditions outside their comfort zones may show altered physiological and behavioural responses, which may consequently cause productive losses. For these reasons, the aim of this paper is to develop a decision tree for the prediction of respiratory rate (RR, mov min_1) of piglets exposed to different thermal situations. The experiment was carried out in an experimental pig farm of the Universidad Nacional de Colombia Campus Medellin, located at the San Pablo Agraria Experimental Station located in the eastern sector of the department of Antioquia, during August 2019. A database containing the raw data for dry bulb temperature - tdb (°C), and relative humidity - RH (%) as input variables, and RR (mov min-1) of six piglets were assessed every two hours as output variable for piglets was generated. The experimental database was composed of 78 observed data. The decision trees were developed to conditions of tdb between 19.2 to 29.5 °C and RH between 50.2 to 88.4%. In the experimental period, RR of piglets submitted to tdb higher than 27.1 °C the RR was around 60 mov min-1, tdb smaller than 27.1 °C the RR varied from 36 to 46 mov min-1. These low values of physiological responses may indicate that the piglets are not in a comfortable situation, so their development, welfare and production can be affected. The decision tree developed can be useful to provide a quick understanding of the piglet's welfare condition based on the environmental variables and physiological responses. [ABSTRACT FROM AUTHOR]

On October 26, 2016, a large-scale mass movement occurred surrounding the Medellín-Bogotá highway, near the municipality of Copacabana. This paper investigates the physical and legal conditions of the place where the landslide occurred, and thus, to corroborate the causes, the possibility of avoiding or preventing the phenomenon and the influence of anthropic activities in the sector. To determine the susceptibility to mass movements and verify the physical conditions, it was carried out by means of geological mapping, geomorphology and interpretation of satellite images. The legal and bibliographic review was consulted in the reports of the event, the PBOT of the municipality of Copacabana and the mining file of the Cantera Las Nieves. The area was found to be highly susceptible to mass movements, and exploitation in the Cantera Las Nieves triggered the landslide, which could be avoided. Similar phenomena could occur in the near future in the area. [ABSTRACT FROM AUTHOR]

This paper summarizes the different activities undertaken in order to identify the main causes of the collapse of the Space Building, a 27-story reinforced concrete framed building in Medellín, Colombia. Site investigations, field and laboratory tests, and a detailed analytical work including structural analysis under different considerations were carried out. The main cause of the collapse was associated with the deficient structural capacities of the columns of the building in relation to the dead and service loads imposed at the time of the collapse. The occurrence of high differential settlements, the high compressive stresses in main columns, the repair works that were carried out minutes before the collapse, and the probable shear failure of beams and compressive failure of partitions walls during repair works were identified as probable secondary causes and detonating factors of the building collapse. The authors of this investigation considered that if the building had been designed adequately using the Colombian building design codes available at that time, it would not have collapsed, even if most of the aforementioned detonating events had occurred. [ABSTRACT FROM AUTHOR]

Traffic accidents are of worldwide concern, as they are one of the leading causes of death globally. One policy designed to cope with them is the design and deployment of road safety systems. These aim to predict crashes based on historical records, provided by new Internet of Things (IoT) technologies, to enhance traffic flow management and promote safer roads. Increasing data availability has helped machine learning (ML) to address the prediction of crashes and their severity. The literature reports numerous contributions regarding survey papers, experimental comparisons of various techniques, and the design of new methods at the point where crash severity prediction (CSP) and ML converge. Despite such progress, and as far as we know, there are no comprehensive research articles that theoretically and practically approach the model selection problem (MSP) in CSP. Thus, this paper introduces a bibliometric analysis and experimental benchmark of ML and automated machine learning (AutoML) as a suitable approach to automatically address the MSP in CSP. Firstly, 2318 bibliographic references were consulted to identify relevant authors, trending topics, keywords evolution, and the most common ML methods used in related-case studies, which revealed an opportunity for the use AutoML in the transportation field. Then, we compared AutoML (AutoGluon, Auto-sklearn, TPOT) and ML (CatBoost, Decision Tree, Extra Trees, Gradient Boosting, Gaussian Naive Bayes, Light Gradient Boosting Machine, Random Forest) methods in three case studies using open data portals belonging to the cities of Medellín, Bogotá, and Bucaramanga in Colombia. Our experimentation reveals that AutoGluon and CatBoost are competitive and robust ML approaches to deal with various CSP problems. In addition, we concluded that general-purpose AutoML effectively supports the MSP in CSP without developing domain-focused AutoML methods for this supervised learning problem. Finally, based on the results obtained, we introduce challenges and research opportunities that the community should explore to enhance the contributions that ML and AutoML can bring to CSP and other transportation areas. [ABSTRACT FROM AUTHOR]

The adoption of e-commerce by a population requires an analysis of its social, cultural and economic environment, so it is necessary to propose and validate models based on the particularities of each region. From this need arises present research with the objective to examine the factors of the E-Commerce adoption in university students of the Medellin city, through the technology acceptance model, adding the variables of Perceived Security and Trust. The quantitative methodological design consisted of a cross-sectional exploratory research study in order to validate the model. A quantitative self-administered questionnaire was applied to 369 university students from the city of Medellin. To check the validity of the measurement scale used, it was developed to confirmatory factor analysis. Various hypotheses were collected and the degree of association was measured by the statistical Somers' D and Cramer's V. In this study, the attitude is correlated in 0.61 with the intention, being the variable with the greatest impact. Furthermore, the ease of use, perceived usefulness and trust, are antecedent of online shopping intention [ABSTRACT FROM AUTHOR]

This paper analyses how the institutional processes, the school management as well as the contexts in which the school and the students are circumscribed, could impact the performance in the high-school graduation-year test (ICFES). A survey was applied with the purpose of know the actions and interactions among educative process actors. Factor Analysis and Hierarchical econometric models were used with the purpose of construct indexes and measure the impact of the considered variables. We found that school level variables, including management, explain a high percentage of the performance variability, while those constructed variables associated to student's characteristics have a low impact. And parent human capital and the initial student conditions are much more important than the teacher human capital. Contrary to the expected, it's not clear what kind of use and if to increase infrastructure resources favors the achievement. [ABSTRACT FROM AUTHOR]

Education for sustainable development in universities provides the university's community (i.e. students, administrative and academic staff) with the skills and capabilities needed to contribute to the achievement of sustainable development. Education for sustainable development has been introduced into curricula, research, outreach activities and campus operations. Several tools exist nowadays to assess this incorporation. Most of them are focused on the environmental aspect of campus operations and within academic issues but do not consider the entire system. This research aimed to propose an integration of sustainability throughout the university as a system, considering existing tools and frameworks, and proving the theoretical proposal in an empirical context. Thus, this paper focuses on sustainability from a whole-institution perspective, analysing the university as a system conformed of two subsystems, the academic and the administrative. The research used a sequential, mixed-method. The quantitative phase undertook a literature review using bibliometric and content analysis, followed by a qualitative phase using the thematic analysis method to develop the University Sustainability concept. The University Sustainability analysis integrated education for sustainable development aspects for the academic subsystem and the corporate sustainability paradigm for the administrative subsystem. Consequently, a sustainability measurement scale for the University Sustainability concept was developed and proved using the exploratory factor analysis. The survey was applied to directors, academic and administrative staff, and under and post-graduate students of two private universities located in Medellin, Colombia. A total of 1799 useable responses were collected and analysed with SPSS software. The analysis' outcome exposed all factors loaded above 0.40, and overall, the alpha coefficient was 0.930. Results revealed the reliability and validity of the instrument. Hence, the University Sustainability concept was validated, and its measurement scale is suitable to be used in assessing the sustainability of universities holistically. The evidence from the studied universities shows the relevance of corporate sustainability and social issues in the developed University Sustainability concept. • University sustainability considered the academic and administrative subsystems. • Administrative subsystem was highly related with corporate sustainability. • Academic subsystem embraced education for sustainable development in higher education. • High reliability measurement scale assesses holistically sustainability in universities. [ABSTRACT FROM AUTHOR]

This paper presents an electrical infrastructure planning method for transit systems that operate with partially grid-connected vehicles incorporating on-board batteries. First, the state-of-the-art of electric transit systems that combine grid-connected and battery-based operation is briefly described. Second, the benefits of combining a grid connection and battery supply in Bus Rapid Transit (BRT) systems are introduced. Finally, the planning method is explained and tested in a BRT route in Medellin, Colombia, using computational simulations in combination with real operational data from electric buses that are currently operating in this transit line. Unlike other methods and approaches for Battery Electric Bus (BEB) infrastructure planning, the proposed technique is system-focused, rather than solely limited to the vehicles. The objective of the technique, from the vehicle's side, is to assist the planner in the correct sizing of batteries and power train capacity, whereas from the system side the goal is to locate and size the route sections to be electrified. These decision variables are calculated with the objective of minimizing the installed battery and achieve minimum Medium Voltage (MV) network requirements, while meeting all technical and reliability conditions. The method proved to be useful to find a minimum feasible cost solution for partially electrifying a BRT line with In-motion Charging (IMC) technology. [ABSTRACT FROM AUTHOR]