Universidad EAFIT

Economic history is about the performance of economies through time. The objective of research in the field is not only to shed new light on the economic past but also to contribute to economic theory by providing an analytical framework that will enable us to understand economic change. A theory of economic dynamics comparable in precision to general equilibrium theory would be the ideal tool of analysis. In the absence of such a theory we can describe the characteristics of past economies, examine the performance of economies at various times, and engage in comparative static analysis; but missing is an analytical understanding of the way economies evolve through time.

The natural conservation of coastal lagoons is important not only for their ecological importance, but also because of the valuable ecosystem services they provide for human welfare and wellbeing. Coastal lagoons are shallow semi-enclosed systems that support important habitats such as wetlands, mangroves, salt-marshes and seagrass meadows, as well as a rich biodiversity. Coastal lagoons are also complex social-ecological systems with ecosystem services that provide livelihoods, wellbeing and welfare to humans. This study assessed, quantified and valued the ecosystem services of 32 coastal lagoons. The main findings of the study are: (i) the definitions of ecosystem services are still not generally accepted; (ii) the quantification of ecosystem services is made in many different ways, using different units; (iii) the evaluation in monetary terms of some ecosystem service is problematic, often relying on non-monetary evaluation methods; (iv) when ecosystem services are valued in monetary terms, this may represent very different human benefits; and, (v) different aspects of climate change, including increasing temperature, sea-level rise and changes in rainfall patterns threaten the valuable ecosystem services of coastal lagoons.

Abstract Nowadays, there are plenty of studies that seek to determine which are the skills that should be met by an engineer. Communication and teamwork are some of the most recurrent ones associated with a knowledge of the engineering sciences. However, their application is not straight forward, due to the lack of educational approaches that contributes to develop experience-based knowledge. Learning Factories (LF) have shown to be effective for developing theoretical and practical knowledge in a real production environment. This article describes the transformation process of a training-addressed manufacturing workshop, in order to structure a Learning Factory for the production engineering program at EAFIT University. The proposed transformations were based on the definition of three pillars (didactic, integrative and engineering) for the development of an LF. We argue that a proper transformation process may contribute to ease the path towards new manufacturing trends such as industry 4.0 into an academic context that strengths the engineering training process.

The economic approach My research uses economic approach to analyze social issues that range beyond those usually considered by economists. This lecture will describe the approach, and illustrate it with examples drawn from past and current work.

The aim objective of this paper is oriented to develop the necessaries processes and elements to use the case study, as a methodological tool of scientific research. Specifically, to demonstrate his key characteristics, his value, benefit and practice utility, as well as the way to overcome to the generated debate around him, respect no scientific validity and liability which is associated to quantitative methods.

Buildings are one of the main consumers of energy in cities, which is why a lot of research has been generated around this problem. Especially, the buildings energy management systems must improve in the next years. Artificial intelligence techniques are playing and will play a fundamental role in these improvements. This work presents a systematic review of the literature on researches that have been done in recent years to improve energy management systems for smart building using artificial intelligence techniques. An originality of the work is that they are grouped according to the concept of “Autonomous Cycles of Data Analysis Tasks”, which defines that an autonomous management system requires specialized tasks, such as monitoring, analysis, and decision-making tasks for reaching objectives in the environment, like improve the energy efficiency. This organization of the work allows us to establish not only the positioning of the researches, but also, the visualization of the current challenges and opportunities in each domain. We have identified that many types of researches are in the domain of decision-making (a large majority on optimization and control tasks), and defined potential projects related to the development of autonomous cycles of data analysis tasks, feature engineering, or multi-agent systems, among others.

ABSTRACT In this paper, we introduce a new spatially constrained clustering problem called the max‐ p ‐regions problem. It involves the clustering of a set of geographic areas into the maximum number of homogeneous regions such that the value of a spatially extensive regional attribute is above a predefined threshold value. We formulate the max‐ p ‐regions problem as a mixed integer programming (MIP) problem, and propose a heuristic solution.

BACKGROUND: There is no effective therapy for the severe acute respiratory syndrome by coronavirus 2 (SARS-CoV2) responsible for the Coronavirus disease 2019 (Covid-19). To date, dexamethasone has shown a decrease in mortality in patients who require oxygen, especially those with invasive mechanical ventilation. However, it is unknown if another corticosteroid can be used, the optimal dose and its duration, to achieve a better clinical outcome. The objective of the study was to compare the differences in clinical outcome and laboratory results in hospitalized patients with severe SARS-CoV2 Pneumonia treated with dexamethasone at 6 mg doses versus patients treated with high-dose methylprednisolone. MATERIALS AND METHODS: Ambispective cohort study with survival analysis of 216 patients diagnosed with severe Covid-19 pneumonia confirmed by polymerase chain reaction for SARS-CoV2 by Berlin protocol, who were hospitalized in a high-complexity clinic in Medellín, Colombia. The patients should also have supplementary oxygen and radiological confirmation of Pneumonia by chest tomography. Sample size was not calculated since the total population that met the inclusion criteria was evaluated. 111 patients were treated with the institutional protocol with intravenous dexamethasone 6 mg QD for seven to 10 days if they required oxygen. Since September 15, 2020, the hospitalization protocol of the clinic was modified by the Infectious Diseases and Pulmonology service, recommending a high dose of methylprednisolone of 250 to 500 mg every day for three days with a subsequent change to oral prednisone 50 mg every day for 14 days. The protocol was not applied in the intensive care unit, where dexamethasone continued to be administered. The clinical outcome and differences in laboratory results of the patients who received dexamethasone vs. the prospective cohort that received methylprednisolone from September 15 to October 31, 2020, were evaluated. Follow-up was carried out by outpatient consultation one month after discharge or by telephone, inquiring about readmission or living-dead status. RESULTS: 216 patients had Covid-19 pneumonia documented by ground-glass imaging and alveolar pressure / inspired oxygen fraction (PaFi) less than 300. 111 patients received dexamethasone (DXM) and 105 received methylprednisolone (MTP). Patients in the DXM group evolved to severe ARDS in a higher proportion (26.1% vs 17.1% than the MTP group). Upon completion 4 days of treatment with parenteral corticosteroid, laboratory markers of severity decreased significantly in the group that received MTP, CRP 2.85 (2.3-3.8) vs 7.2 (5.4-9.8), (p-value < 0.0001), D-dimer 691 (612-847) vs 1083 (740-1565) (p-value = 0.04) and DHL 273 (244-289) vs 355 (270.6-422) (p-value = 0.01). After starting the corticosteroid, transfer to the intensive care unit (4.8% vs. 14.4%) and mortality (9,5% vs. 17.1%) was lower in the group that received MTP. Recovery time was shorter in patients treated with MTP, three days (3-4) vs. DXM 6 days (5-8) (p-value < 0.0001). At 30-day follow-up, 88 (92.6%) were alive in MTP vs 58 (63.1%) of those who received dexamethasone. CONCLUSIONS: In this study, the treatment of severe Covid-19 Pneumonia with high-dose methylprednisolone for three days followed by oral prednisone for 14 days, compared with 6 mg dexamethasone for 7 to 10 days, statistically significantly decreased the recovery time, the need for transfer to intensive care and the severity markers C-reactive protein (CRP), D-dimer and LDH. Randomized controlled studies with methylprednisolone are required to corroborate its effect, and studies in a population hospitalized in intensive care wards.

My research uses economic approach to analyze social issues that range beyond those usually considered by economists. This lecture will describe the approach, and illustrate it with examples drawn from past and current work.

Since 2015, the Global Earthquake Model (GEM) Foundation and its partners have been supporting regional programs and bilateral collaborations to develop an open global earthquake risk model. These efforts led to the development of a repository of probabilistic seismic hazard models, a global exposure dataset comprising structural and occupancy information regarding the residential, commercial and industrial buildings, and a comprehensive set of fragility and vulnerability functions for the most common building classes. These components were used to estimate probabilistic earthquake risk globally using the OpenQuake‐engine, an open‐source software for seismic hazard and risk analysis. This model allows estimating a number of risk metrics such as annualized average losses or aggregated losses for particular return periods, which are fundamental to the development and implementation of earthquake risk mitigation measures.

ABSTRACT: The proportion of food expenditure in the home with respect to total household expenditure is an inverse indicator to wellbeing. Objective: To analyze the differences in the proportion of food expenditure in Medellin households according to socioeconomic characteristics and food security classifications. Materials and Methods: A cross-sectional study in 3008 households in Medellin that participated in the 2015 Food and Nutrition Security Profile in Medellin. Food expenditure was analyzed according to food security, characteristics of the head of household, location of living space, socioeconomic strata, and places where purchases are made. The statistical tests chi-square, Spearman and ordinal logistic regression were applied. Results: Households classified as food-insecure presented with a higher proportion of food expenditure (p=0.000). An inverse relationship was found between the proportion of household expenditure and income and education level of the head of household (p=0.00). Also, a higher probability was found between high proportion of food expenditure and households of low socioeconomic status (p=0.00), and households located in rural areas. Households with a higher proportion of food expenditure are more likely to purchase foods at local stores and shops (p=0.00). Conclusion: There is a greater proportion of food expenditure in Medellin households of low socioeconomic status, located in rural areas, with low educational level of the head of household, and who make purchases in local stores.

The Magdalena River, a major fluvial system draining most of the Colombian Andes, is a world-class river, in the top 10 in terms of sediment load (approximately 150 MT/yr). In this study, we explore the major natural factors and anthropogenic influences behind the patterns in sediment yield on the Magdalena basin and reconstruct the spatial and temporal pattern of deforestation and agricultural intensification across the basin to test the relationships between land use change and trends in sediment yield. Our results show that sediment yield for the whole Magdalena catchment can be explained by natural variables, including runoff and maximum water discharge. These two estimators explain 58% of variance in sediment yield. Temporal analyses of sediment discharges and land use show that the extent of erosion within the catchment has increased over the last 10 to 20 years. Many anthropogenic influences, including a forest decrease by 40% in a 20-year period, an agriculture and pasture increase by 65%, poor soil conservation and mining practices, and increasing rates of urbanization, may have accounted for the overall increasing trends in sediment yield on a regional scale.

BACKGROUND: The World Health Organization declared the ongoing Zika virus (ZIKV) epidemic in the Americas a Public Health Emergency of International Concern on February 1, 2016. ZIKV disease in humans is characterized by a "dengue-like" syndrome including febrile illness and rash. However, ZIKV infection in early pregnancy has been associated with severe birth defects, including microcephaly and other developmental issues. Mechanistic models of disease transmission can be used to forecast trajectories and likely disease burden but are currently hampered by substantial uncertainty on the epidemiology of the disease (e.g., the role of asymptomatic transmission, generation interval, incubation period, and key drivers). When insight is limited, phenomenological models provide a starting point for estimation of key transmission parameters, such as the reproduction number, and forecasts of epidemic impact. METHODS: We obtained daily counts of suspected Zika cases by date of symptoms onset from the Secretary of Health of Antioquia, Colombia during January-April 2016. We calibrated the generalized Richards model, a phenomenological model that accommodates a variety of early exponential and sub-exponential growth kinetics, against the early epidemic trajectory and generated predictions of epidemic size. The reproduction number was estimated by applying the renewal equation to incident cases simulated from the fitted generalized-growth model and assuming gamma or exponentially-distributed generation intervals derived from the literature. We estimated the reproduction number for an increasing duration of the epidemic growth phase. RESULTS: The reproduction number rapidly declined from 10.3 (95% CI: 8.3, 12.4) in the first disease generation to 2.2 (95% CI: 1.9, 2.8) in the second disease generation, assuming a gamma-distributed generation interval with the mean of 14 days and standard deviation of 2 days. The generalized-Richards model outperformed the logistic growth model and provided forecasts within 22% of the actual epidemic size based on an assessment 30 days into the epidemic, with the epidemic peaking on day 36. CONCLUSION: Phenomenological models represent promising tools to generate early forecasts of epidemic impact particularly in the context of substantial uncertainty in epidemiological parameters. Our findings underscore the need to treat the reproduction number as a dynamic quantity even during the early growth phase, and emphasize the sensitivity of reproduction number estimates to assumptions on the generation interval distribution.

Global constitutionalism is an agenda that identifies and advocates for the application of constitutionalist principles in the international legal sphere. Global constitutionalization is the gradual emergence of constitutionalist features in international law. Critics of global constitutionalism doubt the empirical reality of constitutionalization, call into question the analytic value of constitutionalism as an academic approach, and fear that the discourse is normatively dangerous because it is anti-pluralist, artificially creates a false legitimacy, and promises an unrealistic end of politics. This article addresses these objections. I argue that global constitutionalization is likely to compensate for globalization-induced constitutionalist deficits on the national level, that a constitutionalist reading of international law can serve as a hermeneutic device, and that the constitutionalist vocabulary uncovers legitimacy deficits of international law and suggests remedies. Global constitutionalism, therefore, has a responsibilizing and much-needed critical potential.

National parks and other protected areas are at the forefront of global efforts to protect biodiversity and ecosystem services. However, not all protection is equal. Some areas are assigned strict legal protection that permits few extractive human uses. Other protected area designations permit a wider range of uses. Whether strictly protected areas are more effective in achieving environmental objectives is an empirical question: although strictly protected areas legally permit less anthropogenic disturbance, the social conflicts associated with assigning strict protection may lead politicians to assign strict protection to less-threatened areas and may lead citizens or enforcement agents to ignore the strict legal restrictions. We contrast the impacts of strictly and less strictly protected areas in four countries using IUCN designations to measure de jure strictness, data on deforestation to measure outcomes, and a quasi-experimental design to estimate impacts. On average, stricter protection reduced deforestation rates more than less strict protection, but the additional impact was not always large and sometimes arose because of where stricter protection was assigned rather than regulatory strictness per se. We also show that, in protected area studies contrasting y management regimes, there are y2 policy-relevant impacts, rather than only y, as earlier studies have implied.

Purpose This paper aims to summarize previous research findings on the mutual relation between digital transformation and sustainability at a firm-level. Up to date, there is a gap in the literature linking both concepts and a generalized call for more studies. Design/methodology/approach This research uses a systematic literature review of 89 published studies. After detailed content analysis filters, the authors used 75. The authors present the results following the “Six W” guidelines for systematic literature reviews. Findings Findings reveal that it is possible to suggest a research framework that considers digital transformation as a driver and a predecessor of sustainability. To survive the digital revolution, companies need to enhance their digital capabilities and balance their economic, environmental and social impacts. Research limitations/implications The precision of the equation used to search manuscripts might have excluded some critical studies that analyze both topics with different connotations beyond merely “Digital transformation” and “Sustainability.” Moreover, the heterogeneity of the findings makes it difficult to classify the findings in a specific context. Originality/value The present paper serves as a base to understand the implications of digital transformation on sustainable development for businesses and societies.

Artificial intelligence (AI) is playing a leading role in the digital transformation of enterprises, particularly in the manufacturing industry where it has been responsible for a profound transformation in key business and production operations. Despite the accelerated growth of AI technologies, knowledge of the implementation of AI by small and medium-sized enterprises (SMEs) remains underexplored. Thus, this study seeks to examine how manufacturing SMEs orchestrate resources for AI implementation. Building on the resource orchestration (RO) theory and recent work on AI implementation, we investigate multiple case studies involving manufacturing SMEs in Sweden operating in the packaging, plastic, and metal sectors. Our findings indicate that SMEs structure a portfolio based on acquiring and accumulating AI resources. AI resources are bundled into learning and governance capabilities to leverage configurations for AI implementation. Through a dynamic process of AI resource orchestration, SMEs effectively leverage AI resources and capabilities by mobilising technologies, coordinating manufacturing processes, and empowering skilled people. This research contributes to existing practice and the academic literature on AI implementation, highlighting how SMEs orchestrate AI resources and capabilities to drive an organisation’s digital transformation whilst creating a competitive advantage.

Existing workflows for the analysis of multi-omic microbiome datasets are lab-specific and often result in sub-optimal data usage. Here we present IMP, a reproducible and modular pipeline for the integrated and reference-independent analysis of coupled metagenomic and metatranscriptomic data. IMP incorporates robust read preprocessing, iterative co-assembly, analyses of microbial community structure and function, automated binning, as well as genomic signature-based visualizations. The IMP-based data integration strategy enhances data usage, output volume, and output quality as demonstrated using relevant use-cases. Finally, IMP is encapsulated within a user-friendly implementation using Python and Docker. IMP is available at http://r3lab.uni.lu/web/imp/ (MIT license).

South America—in particular, the Andean countries—are exposed to high levels of seismic hazard, which, when combined with the elevated concentration of population and properties, has led to an alarming potential for human and economic losses. Although several fragility models have been developed in recent decades for South America, and occasionally used in probabilistic risk analysis, these models have been developed using distinct methodologies and assumptions, which renders any direct comparison of the results across countries questionable, and thus application at a regional level unreliable. This publication aims at obtaining a uniform fragility model for the most representative building classes in the Andean region, for large‐scale risk analysis. To this end, sets of single‐degree‐of‐freedom oscillators were created and subjected to a series of ground motion records using nonlinear time history analyses, and the resulting damage distributions were used to derive sets of fragility functions.

Non-fungible tokens (NFTs) can be used to represent ownership of digital art or any other unique digital item where ownership is recorded in smart contracts on a blockchain. NFTs have recently received enormous attention from both cryptocurrency investors and the media. We examine why NFTs have gotten so much attention. Using vector autoregressive models, we show that Bitcoin returns significantly predict next week’s NFT growth in popularity, measured by Google search queries. Moreover, wavelet coherence analysis suggests that Bitcoin and Ether returns are significant drivers of next week’s attention to NFTs. These results indicate that the remarkable increases in prices of major cryptocurrencies can explain the hype around NFTs.