Cluster competitiveness modeling: approach with systems dynamics

This study makes a systemic review to cluster and create a competitiveness relationship considering a systems dynamics approach. A dynamic hypothesis was constructed to validate what factors increase a cluster’s level of competitiveness, through causal analysis. Then, the causal diagram that validates the dynamic H0 hypothesis was constructed in Vensim PLE systems®. Literature review shows the evolution of the cluster system according to the current needs of the market, and emphasizes the need for new approaches and models that capture the complexity and dynamics of this system, allowing the understanding of its structure and the evaluation of the contribution of factors and capabilities to cluster competitiveness. It highlights the usefulness of systems dynamics as a simulation methodology for dynamic and complex systems, and establishes itself as a growing line of research applied to various systems of study. Dynamic hypothesis H0 was validated using the causal diagram, reaching the conclusion that innovation, productive management, financial management, organizational management, commercial management, and cluster management factors positively increase the cluster competitiveness level. From structure analysis, the behavior is associated to the archetype “Path Dependence,” usual in growing industrial markets

Big Data and automatic detection of topics: Social network texts

This paper proposes the analysis of the influence of terms that express feelings in the automatic detection of topics in social networks. This proposal uses an ontology-based methodology which incorporates the ability to identify and eliminate those terms that present a sentimental orientation in social network texts, which can negatively influence the detection of topics. To this end, two resources were used to analyze feelings in order to detect these terms. The proposed system was evaluated with real data sets from the Twitter and Facebook social networks in English and Spanish respectively, demonstrating in both cases the influence of sentimentally oriented terms in the detection of topics in social network texts

Behavioral aspects of phlebotomine sand flies associated with a case of cutaneous leishmaniasis in Atlántico, northern Colombia

After the first autochthonous case of cutaneous leishmaniasis was reported in the Atlántico department in the Caribbean region of Colombia, entomological sampling was conducted in the specific areas where the infection might have occurred. CDC traps were installed inside and outside dwellings in the peri-urban and rural areas of a settlement in the municipality of Luruaco. Sampling was performed during the night with protected human bait, and phlebotomine sand flies were actively sampled from potential diurnal resting sites within dwellings. Ten species of the genus Lutzomyia were identified; Lutzomyia evansi was the dominant species (78%) in the rural and peri-urban areas as well as in the different sampled habitats, followed by Lutzomyia panamensis and Lutzomyia gomezi. There was a 100% household infestation by Lu. evansi, and its indoor mean abundance was 13.3 sand flies/CDC trap/night. The indoor mean abundance of Lu. panamensis and Lu. gomezi was only 0.9 and 0.8 sand flies/CDC trap/night, respectively. Female Lu. evansi were collected with protected human bait, mostly in the peridomestic area, with sustained activity during the night and a slight increase in the activity from 19:00 to 23:00 hours. Of the total sand flies captured in the diurnal resting sites, 73.1% were collected from the walls of bedrooms and corresponded to Lu. evansi, Lutzomyia cayennensis cayennensis, and Lutzomyia trinidadensis. Owing to their vectorial importance, the species on which entomological surveillance should be focused are Lu. evansi, Lu. panamensis, and Lu. gomezi. The biting and resting behavior reported in this study will help guide vector prevention and the control of leishmaniasis within the study area

Artificial intelligence assisted Mid-infrared laser spectroscopy in situ detection of petroleum in soils

A simple, remote-sensed method of detection of traces of petroleum in soil combining artificial intelligence (AI) with mid-infrared (MIR) laser spectroscopy is presented. A portable MIR quantum cascade laser (QCL) was used as an excitation source, making the technique amenable to field applications. The MIR spectral region is more informative and useful than the near IR region for the detection of pollutants in soil. Remote sensing, coupled with a support vector machine (SVM) algorithm, was used to accurately identify the presence/absence of traces of petroleum in soil mixtures. Chemometrics tools such as principal component analysis (PCA), partial least square-discriminant analysis (PLS-DA), and SVM demonstrated the e ectiveness of rapidly di erentiating between di erent soil types and detecting the presence of petroleum traces in di erent soil matrices such as sea sand, red soil, and brown soil. Comparisons between results of PLS-DA and SVM were based on sensitivity, selectivity, and areas under receiver-operator curves (ROC). An innovative statistical analysis method of calculating limits of detection (LOD) and limits of decision (LD) from fits of the probability of detection was developed. Results for QCL/PLS-DA models achieved LOD and LD of 0.2% and 0.01% for petroleum/soil, respectively. The superior performance of QCL/SVM models improved these values to 0.04% and 0.003%, respectively, providing better identification probability of soils contaminated with petroleum

Neural networks for tea leaf classification

The process of classification of the raw material, is one of the most important procedures in any tea dryer, being responsible for ensuring a good quality of the final product. Currently, this process in most tea processing companies is usually handled by an expert, who performs the work manually and at his own discretion, which has a number of associated drawbacks. In this work, a solution is proposed that includes the planting, design, development and testing of a prototype that is able to correctly classify photographs corresponding to samples of raw material arrived at a dryer, using intelligence techniques (IA) type supervised for Classification by Artificial Neural Networks and not supervised with K-means Grouping for class preparation. The prototype performed well and is a reliable tool for classifying the raw material slammed into tea dryers

Interaction with soil bacteria affects the growth and amino acid content of piriformospora indica

Exploration of the e ect of soil bacteria on growth and metabolism of beneficial root endophytic fungi is relevant to promote favorable associations between microorganisms of the plant rhizosphere. Hence, the interaction between the plant-growth-promoting fungus Piriformospora indica and di erent soil bacteria was investigated. The parameters studied were fungal growth and its amino acid composition during the interaction. Fungus and bacteria were confronted in dual cultures in Petri dishes, either through agar or separated by a Perspex wall that only allowed the bacterial volatiles to be e ective. Fungal growth was stimulated by Azotobacter chroococcum, whereas Streptomyces anulatus AcH 1003 inhibited it and Streptomyces sp. Nov AcH 505 had no e ect. To analyze amino acid concentration data, targeted metabolomics was implemented under supervised analysis according to fungal-bacteria interaction and time. Orthogonal partial least squares-discriminant analysis (OPLS-DA) model clearly discriminated P. indica–A. chroococcum and P. indica–S. anulatus interactions, according to the respective score plot in comparison to the control. The most observable responses were in the glutamine and alanine size groups: While Streptomyces AcH 1003 increased the amount of glutamine, A. chroococcum decreased it. The fungal growth and the increase of alanine content might be associated with the assimilation of nitrogen in the presence of glucose as a carbon source. The N-fixing bacterium A. chroococcum should stimulate fungal amino acid metabolism via glutamine synthetase-glutamate synthase (GS-GOGAT). The data pointed to a stimulated glycolytic activity in the fungus observed by the accumulation of alanine, possibly via alanine aminotransferase. The responses toward the growth-inhibiting Streptomyces AcH 1003 suggest an (oxidative) stress response of the fungus

Knowledge on prevention of sexual transmission infections in inmigrant adolescents in Soledad Atlantico 2018-2019

Durante los últimos años ha incrementado el interés en el estudio de la adolescencia y el debut de las relaciones sexuales y de pareja que presentan con frecuencia malestares de la salud sexual y reproductiva, que se manifiesta en el incremento de las infecciones de transmisión sexual y embarazos no deseados. A su vez la problemática por el fenómeno de migración hace que en la mayoría de casos esta población tenga difícil acceso a una institución de salud, lo que no permite tener un diagnóstico precoz y tratamiento oportuno este trabajo tiene como objetivo evaluar el conocimiento sobre prevención de infecciones de transmisión sexual en adolescentes inmigrantes habitantes en Soledad-Atlántico 2018-2019. Se utilizó una metodología cuantitativa con enfoque descriptivo cuantitativo, la población objeto de estudio fueron 234 adolescentes inmigrantes que en el rango de 10-19 años y que accedían a los servicios en salud en institución pública de Soledad-Atlántico. Se logró deducir a través de la encuesta el conocimiento que tienen las adolescentes sobre infecciones de transmisión sexual, el resultado indica que el nivel de conocimiento es medio, y afirman que es de gran importancia que los jóvenes adquieran conocimiento sobre las ITS. Es apremiante la educación a la población vulnerable como método de prevención y aporte a la salud con énfasis en el fortalecimiento de la Educación Integral de la Sexualidad a través de la promoción de conductas sexuales saludables.In recent years, interest in the study of adolescence and the debut of sexual and couple relationships that frequently present sexual and reproductive health discomforts, which is manifested in the increase in sexually transmitted infections and pregnancies, have increased not wanted. At the same time, the problem caused by the phenomenon of migration means that in most cases this population has difficult access to a health institution, which does not allow for early diagnosis and timely treatment. This work aims to assess the knowledge about prevention of sexually transmitted infections in immigrant adolescents living in Soledad-Atlántico 2018-2019. A quantitative methodology with a quantitative descriptive approach was used, the population under study were 234 immigrant adolescents in the range of 10-19 years and accessing health services at a public institution in Soledad-Atlántico. It was possible to deduce through the survey the knowledge that adolescents have about sexually transmitted infections, the result indicates that the level of knowledge is medium, and they affirm that it is of great importance that young people acquire knowledge about STIs. Education to the vulnerable population is urgent as a method of prevention and contribution to health with emphasis on strengthening the Integral Education of Sexuality through the promotion of healthy sexual behaviors

Natural language explanation model for decision trees

This study describes a model of explanations in natural language for classification decision trees. The explanations include global aspects of the classifier and local aspects of the classification of a particular instance. The proposal is implemented in the ExpliClas open source Web service [1], which in its current version operates on trees built with Weka and data sets with numerical attributes. The feasibility of the proposal is illustrated with two example cases, where the detailed explanation of the respective classification trees is shown

Forecasting electric load demand through advanced statistical techniques

Traditional forecasting models have been widely used for decision-making in production, finance and energy. Such is the case of the ARIMA models, developed in the 1970s by George Box and Gwilym Jenkins [1], which incorporate characteristics of the past models of the same series, according to their autocorrelation. This work compares advanced statistical methods for determining the demand for electricity in Colombia, including the SARIMA, econometric and Bayesian methods

Economic order quantity for perishables with decreasing willingness to purchase during their life cycle

In an inventory management model for perishables, depletion due to interacting with the demand is of importance, but also, damage to products is a relevant variable. This article considers that demand and sales phenomena do not always go hand-in-hand. The demand process relates to the willingness to acquire products in good condition, giving the customer the power to evaluate the quality of the product before an effective purchase takes place. We also considered the cost of disposing of unsold units, besides the conventional costs for storage and procurement. We then proposed a mathematical model to derive the Economic Order Quantity (EOQ) under specific conditions, in order to minimize the expected management cost of perishables, assuming constant demand and linearly decreasing purchase probability during the product life cycle. We proposed several random instances and validate the mathematical model using simulation. We then found the optimal parameters for the inventory policy using a third-order numerical approximation. Last, we developed a sensitivity analysis over the product life cycle to prove that the proposed model approximates to a traditional EOQ model for perishables when life cycle is sufficiently large