Sistema Regional de Información
en línea para Revistas Científicas de América Latina,
el Caribe, España y Portugal

The p-median problem is an excellent tool to solve location facilities problems. There are many applications in which the problem of the p-median can be used, some of these can be; location of fire stations, location of police stations and location of distribution centers, among others. This work introduces the use of A Hybrid VNS / TABU Search Algorithm to determine the correct location of p centers. Experimental results on benchmark and the application to a real case indicate the potential of the proposed approach, which is able to produce good solutions with the use of VNS / TABU Search.

We propose a novel network-level metric called Edge Similarity Index (ESI) to quantify the extent of similarity between any two edges of a complex network with respect to the values for a node-level metric (like centrality metric) of its end vertices. To assess the ESI measure for a complex real-world network with respect to a node-level metric, we propose to first construct a logical network whose vertices are the actual edges of the network (with coordinates corresponding to the normalized node-level metric values of the actual end vertices) and there exists a (logical) edge between two logical vertices if the Euclidean distance between their corresponding coordinates is within a threshold distance. We propose a binary search algorithm to determine the minimum value for this threshold distance () that would result in a connected logical unit-disk graph; the ESI value for the complex network is then computed as . The ESI values range from 0.0 to 1.0; the larger the ESI value with respect to a node-level metric, we claim that more similar are any two edges in the network with respect to the node-level metric values for their end vertices.

Workers around the world face major changes in work organization and industrial relations to fulfill the demands of modern working life. The problem of work stress and obesity have become rather delicate subjects because of the negative consequences it brings for employees and companies. The objectives of this research are perform a descriptive analysis using the SPSS v.23 software on work stress, physical activity, eating habits and obesity in the industry for sample characterization. Methods entail the application of a questionnaire for gathering information, the creation of the database in the software and the accomplishment of the descriptive analysis of each section of the questionnaire with the help of the software. As results, physical activity is that all three dimensions show the degree of sedentariness as values fall below 9. As for the diagnosis of eating habits, from the resulting scores, population shows a diagnosis of food anxiety since all the dimensions of the instrument were among the mean values obtained. As conclusions, manufacturing industries must take preventive actions to reduce them by increasing physical activity and improving eating habits among staff. It is also recommended to implement techniques to improve health, ergonomics and safety practices in the manufacturing industry so it is that all three dimensions engage in more activity that is physical.

Convolutional neural networks (CNN) have been applied in different fields including image recognition. A CNN requires a set of images that will be used to teach to classify it into specific categories. However, the question about how image pre-processing influences CNN accuracy has not yet been answered bluntly. This paper proposes the application of pre-processing methods for the images’ feed to a CNN in order to improve the accuracy of the classification. Two methods of pre-processing are evaluated, quantization and sharpness enhancement. Quantization carries out at 7 levels, and sharpness works with four levels using the discrete wavelet transform. The tests were implemented with two CNN models, LeNet-5 and ResNet-50. In the first part of this paper the methodology and description of the CNN models, as well as the data set are presented. Later the descriptions of the experiments are presented. Finally, it is shown how the proposed method achieved an improvement on the accuracy compared with the results obtained with the images with no modification. The proposed pre-processing methods had an improvement between 1.35 and 3.1% on the validation accuracy.

Fused Filament Fabrication is an Additive Manufacturing technology that is continuously improving its technologies. The present research exposes the use of the Travel Salesman Problem and Nearest Neighbor Algorithm as a strategy to reduce the time need to print slots shield in a Printed Circuit Board. The methodology used describes the process from the design to the printing board. A board of 98 slots of four geometries was analyzed. The results present a reduction of 5.88% of the original time required to print the board.

The NASA-TLX method has been used more than 20 years to assess mental workload. NASA-TLX is a multidimensional technique to provide a score based on six subscales: mental demand, physical demand, temporal demand, performance, effort, and frustration level. Although some researchers have developed software to facilitate the application of the method, all the versions created use a single form of evaluation. Throughout a literature review, four different equations to apply NASA-TLX where identified. The objective of this investigation is to develop the NASA-TLX Multi Equation Tool (MET) programmed with the four different equations. The software was created in HTML format with JavaScript. As a result, the software shows the workload assessment with the four different equations.

The purpose of this work was to distribute the hand grip force between the muscles of the hand by means of a non-linear model solved with static optimization. The force was measured in three pairs of flexion angles corresponding to the shoulder and elbow. (90°-90°, 135°-45°, and 160°- 30). A total of 22 participants completed the study, 6 men and 6 women between 18 and 25 years old. A MicroFET® handgrip dynamometer (0-200 pounds) and an adjustable angle arm support were used during the data collection. The moment arms of the forearm were predicted using a model proposed in the literature. The distribution of force was modeled as a non-linear problem of static optimization and solved with the fmincon optimizer of the Matlab® optimization toolbox. The results obtained in the calculation of the moment arm show that they decrease as the angle of flexion of the elbow decreases. The static optimization model used for the three scenarios related to the flexion angle of the elbow always assigned muscle force to the extensor muscles.

Quick changeover in a production line facilitates the manufacture of small lots and a wide variety of products, where several variables must be controlled to optimize this process. This article presents a structural equation model relating three lean manufacturing tools as independent latent variables: quick setups, small lot production, and uniform production level, which are related to optimize a response variable: quality control. The variables are linked through six hypotheses and validated with the partial least squares technique that is integrated into the WarpPLS 6.0 software. The model is validated with data from 220 responses to a questionnaire applied to the manufacturing industry in northern Mexico. Findings indicate that a quick changeover guarantees small lots, better customization of standardized products, and an appropriate quality for the customer by optimizing the production and delivery time.  

Technological development in recent years has generated the constant need to digitalize and analyze data, where handwritten digit recognition is a popular problem. This paper focuses on the creation of two handwritten digit datasets and their use to train a Convolutional Neural Network (CNN) to classify them, also, a proposed extra preprocessing technique is applied to the images of one of the data sets. Experiments show that the proposed preprocessing technique lead to obtain accuracies above 98%, which were higher than the values obtained with the dataset without the additional preprocessing.

In this investigation, a spectral analysis of the structural response of two reinforced concrete buildings, of 18 m and 56 m of height and with different plan regularity, subjected to ambient vibrations was done. Specifically, natural frequencies and modes of vibrations were determined through applying basic frequency domain (BFD) and frequency domain decomposition (FDD) methods to ambient vibration data acquired by three triaxial accelerometers. At least three vibration modes were identified for each building in a range of frequencies comprehended between 1,0 Hz and 6,0 Hz. Modes with more spectral density were exhibited between frequencies of 1,0 Hz and 3,0 Hz. From mode shapes, it was determined that structures with more plan irregularity presents more torsional modes and coupling of these with flexural modes.