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Augmented Reality is a promising technology to support safe pedestrian evacuation from buildings during earthquakes. The widespread availability of phone cameras makes this idea feasible. QR codes, for instance, can place virtual three-dimensional objects to guide people along a prescribed route. In this work, a prototypical system based on this premise was created in response to the 2020 sudden surge in seismic activity in Puerto Rico. The results point towards feasibility as well as to a series of areas that require improvement for a real-life implementation.

Situations faced in the advent of powerful hurricanes can be stressful for individuals due to the uncertainty they bring along. The consequences of these phenomena can leave individuals’ recovery in their own hands until order is re-established, and support can reach out to them. This work aims to develop a tool to guide individuals through their decision-making during a hurricane disaster and recovery, using the experience of Hurricane María. The tool is a classic inventory model adapted to monitor individual’s wellness through 48 hours after a hurricane arrival. This article presents the three stages followed in the development in this work: the assessment of individuals’ sentiments toward Hurricane María via an online questionnaire, the development of the mathematical model, and the creation of a prototype in the form of a mobile application. Each phase presents an important contribution: a summary of first-hand knowledge obtained from the reactions of individuals who survived Hurricane María, a novel modeling approach to the problem, and a convenient framework that synthesizes both previous components.

Manufacturing is a key economic activity. Its know-how and its competitive advantages have shaped the history of humankind. In many Industrial Engineering (IE) programs, however, manufacturing is not taught as related to process know-how, but as systemic integration and management. It is possible to capitalize on the recent 3D printing technologies to reintroduce IE students to manufacturing principles through prototyping projects. Sparking the interest of IE students, though, can also be achieved through selecting problems of environmental awareness. This work describes a project of self-assemblies developed under the scheme of undergraduate research that makes prototyping a central endeavor with the aim to foster plastic recycling as an end goal.

The focus of this work is to enable the dignified survival of the people affected in a natural disaster through the optimal location of pre-positioned warehouses and the determination of their optimal inventory levels aiming to provide emergency supplies efficiently. To this end, a mixed-integer nonlinear mathematical model is presented to be applied during the disaster preparation phase. The mathematical model integrates two elements immersed in humanitarian logistics; one that is based on determining the optimal locations for the installation of pre-positioned warehouses, and the other that approaches the establishment of the inventory levels in these facilities to supply food, equipment, water, and vaccines to different kinds of groups of survivors classified according to the human life cycle. The mathematical model is validated through the analysis of two regions of the State of Veracruz, the Mexican state with the highest number of declarations of emergency related to hydrometeorological phenomena. The installation of instances with two and three pre-positioned warehouses, each with a total of 29 municipalities, 14 of which have been subject to the issuance of declarations of emergency, are presented. The results show the optimal location for the pre-positioned warehouses and the number of kits allocated to each one depending on the type of demand. The model is a decision support instrument designed for the use of planners for the timely and efficient response to disasters to aid the people affected by a natural phenomenon, especially one of the hydrometeorological type.

Many organizations have considered implementing teamwork as an approach to improve organizational performance and boost the learning process of workers. Despite the benefits offered by teamwork, literature has also shown negative aspects of this kind of work setting, including the transient initial team underachievement known as process loss. Studies have been dedicated to investigate the effect of implementing teamwork strategies on team productivity. However most of these studies remain observational in nature, partially due to the complexity associated with performing physical experimentation in teamwork manufacturing settings and the study of human cognition. The current study proposes the use of simulation as a strategy to conduct experimentation in this kind of setting. This work capitalizes on simulation to investigate the joint effect of knowledge transfer and process loss on team productivity for manufacturing settings. The joint effect of these factors on team productivity still remains unknown in current literature of teamwork.

espanolLos sistemas hidroponicos existen desde hace ya varios anos en una amplia variedad de disenos y configuraciones. El proyecto que aqui se describe comprende el diseno de un sistema hidroponico modular apilable para uso domestico, con miras a que sea uno con alta productividad. Una impresora 3D se utilizo para desarrollar el prototipo que consta de elementos cilindricos, cada uno con tres receptaculos. A los elementos cilindricos apilables se les puede rotar con respecto de su eje, anadiendo asi mayor flexibilidad de configuracion. Convenientemente, la tapa del sistema esta formada por medio elemento cilindrico. Para la prueba preliminar que aqui se describe, se depositaron semillas de cilantro por un periodo de 14 dias en Rockwool. En su fase prototipica, se determino la factibilidad del sistema, aunque se reconoce que es aun necesario optimizar el diseno y afinar su operacion. Esto implicara conciliar dos objetivos en conflicto: maximizar la productividad del sistema y minimizar su costo total EnglishHydroponic systems have been around for many years and there is a large variety of designs. This project approaches the design of a stackable hydroponic system for domestic use aiming for high yield. The 3-D printed prototype consisted of cylindrical elements each with three entries for seeding. These elements can be stacked up and rotated around one another providing configuration flexibility. In addition, the cap of our system is a half cylindrical element. A preliminary test included the use of Rockwool to grow coriander for 14 days. Being a prototype, it is still necessary to optimize the design, fine-tune its operation beyond its initial feasibility to both, maximize yield and minimize cost, which implies compromising on these two conflicting objectives.

In 2017, approximately 5 million Americans were living with Alzheimer’s disease (AD), and it is estimated that by 2050 this number could increase to 16 million. In this study, we apply mathematical optimization to approach microarray analysis to detect differentially expressed genes and determine the most correlated structure among their expression changes. The analysis of {"type":"entrez-geo","attrs":{"text":"GSE4757","term_id":"4757"}}GSE4757 microarray dataset, which compares expression between AD neurons without neurofibrillary tangles (controls) and with neurofibrillary tangles (cases), was casted as a multiple criteria optimization (MCO) problem. Through the analysis it was possible to determine a series of Pareto efficient frontiers to find the most differentially expressed genes, which are here proposed as potential AD biomarkers. The Traveling Sales Problem (TSP) model was used to find the cyclical path of maximal correlation between the expression changes among the genes deemed important from the previous stage. This leads to a structure capable of guiding biological exploration with enhanced precision and repeatability. Ten genes were selected (FTL, GFAP, HNRNPA3, COX1, ND2, ND3, ND4, NUCKS1, RPL41, and RPS10) and their most correlated cyclic structure was found in our analyses. The biological functions of their products were found to be linked to inflammation and neurodegenerative diseases and some of them had not been reported for AD before. The TSP path connects genes coding for mitochondrial electron transfer proteins. Some of these proteins are closely related to other electron transport proteins already reported as important for AD.

Establishing the role that different genes play in the development of cancer is a daunting task. A step toward this end is the detection of genes that are important in the illness from high‐throughput biological experiments. Furthermore, it is safe to say that it is highly unlikely that these show expression changes independently, even with a list of potentially important genes. A biological signaling pathway is a more plausible underlying mechanism as favored in the literature. This work attempts to build a mathematical network problem through the analysis of microarray experiments. A preselection of genes is carried out with a multiple criteria optimization framework previously published by our research group 1. Afterward, application of the Traveling Salesperson Problem and Minimum Spanning Tree network optimization models are proposed to identify potential signaling pathways via the most correlated path among the genes of interest. Biological evidencing is provided to assess the effectiveness of the proposed methods. The capability of our analysis strategy is also demonstrated through the undertaking of meta‐analysis studies. Three important aspects are novel in this work: (1) our joint analyses of different groups of lung cancer states reveal new correlations, biologically evidenced, and previously undocumented; (2) computation of the correlation coefficients from expression differences leads to an effective use of network optimization methods; and (3) the methods yield mathematically optimal correlation structures: no other configuration is better correlated using the available information.

Prediction of lung cancer metastasis relies on post-resection assessment of tumor histology, which is a severe limitation since only a minority of lung cancer patients are diagnosed with resectable disease. Therefore, characterization of metastasis-predicting biomarkers in pre-resection small biopsy specimens is urgently needed. Here we report a biomarker consisting of the phosphorylation of the retinoblastoma protein (Rb) on serine 249 combined with elevated p39 expression. This biomarker correlates with epithelial-to-mesenchymal transition traits in non-small cell lung carcinoma (NSCLC) cells. Immunohistochemistry staining of NSCLC tumor microarrays showed that strong phospho-Rb S249 staining positively correlated with tumor grade specifically in the squamous cell carcinoma (SCC) subtype. Strong immunoreactivity for p39 positively correlated with tumor stage, lymph node invasion, and distant metastases, also in SCC. Linear regression analyses showed that the combined scoring for phospho-Rb S249, p39 and E-cadherin in SCC is even more accurate at predicting tumor staging, relative to each score individually. We propose that combined immunohistochemistry staining of NSCLC samples for Rb phosphorylation on S249, p39, and E-cadherin protein expression could aid in the assessment of tumor staging and metastatic potential when tested in small primary tumor biopsies. The intense staining for phospho-Rb S249 that we observed in high grade SCC could also aid in the precise sub-classification of poorly differentiated SCCs.

Microarrays can provide large amounts of data for genetic relative expression in illnesses of interest such as cancer in short time. These data, however, are stored and often times abandoned when new experimental technologies arrive. This work reexamines lung cancer microarray data with a novel multiple criteria optimization‐based strategy aiming to detect highly differentially expressed genes. This strategy does not require any adjustment of parameters by the user and is capable to handle multiple and incommensurate units across microarrays. In the analysis, groups of samples from patients with distinct smoking habits (never smoker, current smoker) and different gender are contrasted to elicit sets of highly differentially expressed genes, several of which are already associated to lung cancer and other types of cancer. The list of genes is provided with a discussion of their role in cancer, as well as the possible research directions for each of them.

The replacement of cement by mineral admixtures in concrete has been of increasing interest in the construction industry. Nevertheless, several of the potential replacements, such as fly ash class F, lower the compressive strength of concrete at early age. This project investigates the use of nanosilica to compensate for such loss of compressive strength. A statistical experimental design involving mixtures of Portland cement, fly ash and nanosilica, in addition to water/binder ratio as an external factor, is proposed to study their combined effect on the compressive strength of concrete. This design allows estimating a cubic regression model that properly accounts for the effects of the mixture components within a constrained experimental region. The range of each factor was selected according to levels normally used in the industry. Finally, an optimisation strategy permits to recommend the use of nanosilica when high percentages of cement replacement by fly ash are present.

Process windows in injection molding are habitually built with only one performance measure in mind. In reality, a more realistic picture can be obtained when considering multiple performance measures at a time, especially in the presence of conflict. In this work, the construction of process windows for injection molding (IM) is undertaken considering two and three performance measures in conflict simultaneously. The best compromises between the criteria involved are identified through the direct application of the concept of Pareto-dominance in multiple criteria optimization. The aim is to provide a formal and realistic strategy to set processing conditions in IM operations. The resulting optimization approach is easily implementable in MS Excel. The solutions are presented graphically to facilitate their use in manufacturing plants.

Microarray experiments are capable of determining the relative expression of tens of thousands of genes simultaneously, thus resulting in very large databases. The analysis of these databases and the extraction of biologically relevant knowledge from them are challenging tasks. The identification of potential cancer biomarker genes is one of the most important aims for microarray analysis and, as such, has been widely targeted in the literature. However, identifying a set of these genes consistently across different experiments, researches, microarray platforms, or cancer types is still an elusive endeavor. Besides the inherent difficulty of the large and nonconstant variability in these experiments and the incommensurability between different microarray technologies, there is the issue of the users having to adjust a series of parameters that significantly affect the outcome of the analyses and that do not have a biological or medical meaning. In this study, the identification of potential cancer biomarkers from microarray data is casted as a multiple criteria optimization (MCO) problem. The efficient solutions to this problem, found here through data envelopment analysis (DEA), are associated to genes that are proposed as potential cancer biomarkers. The method does not require any parameter adjustment by the user, and thus fosters repeatability. The approach also allows the analysis of different microarray experiments, microarray platforms, and cancer types simultaneously. The results include the analysis of three publicly available microarray databases related to cervix cancer. This study points to the feasibility of modeling the selection of potential cancer biomarkers from microarray data as an MCO problem and solve it using DEA. Using MCO entails a new optic to the identification of potential cancer biomarkers as it does not require the definition of a threshold value to establish significance for a particular gene and the selection of a normalization procedure to compare different experiments is no longer necessary.

Driven by wind and solar photovoltaics technology, the power industry is shifting towards a distributed generation (DG) paradigm. A key challenge in deploying a renewable DG system is the power volatility. This study proposes a visionary energy concept and further presents a mathematical model that could help the large industry consumers adopt this new energy technology. The study seeks to design a grid-connected DG system that is capable of providing the necessary electricity for wafer fabs. Simulation-based optimization algorithm was applied to determine the equipment type and capacity aiming to minimize the DG lifecycle cost. The proposed method was demonstrated on fab facilitates located in three different regions in the US.

ResumenEn este trabajo se presenta un estudio de validación de un método para seleccionar técnicas de pronóstico de series de tiempo. En el método propuesto se utilizan Redes Neuronales Artificiales para predecir el desempeño de varios métodos estadísticos tradicionales de pronóstico para así seleccionar el de mejor potencial. Para llevar a cabo la validación, se emplearon dieciocho series de tiempo reales, correspondientes a actividades económicas del estado de Tamaulipas. Los resultados apuntan a que el método de selección propuesto es suficientemente confiable para devenir un recurso de fácil aplicación para personas con poco conocimiento estadístico. Tablas con los resultados del método se incluyen en este trabajo para hacer más conveniente la identificación del método estadístico tradicional de pronóstico sugerido.AbstractIn this paper, a validation study for a method geared towards the selection of forecasting techniques for time series is presented. The proposed method makes use of artificial neural networks to predict the performance of several statistics-based forecasting techniques to help select the potential best one. Eighteen time series with real data related to economic activities in the state of Tamaulipas were used for validation purposes. The results indicate that the proposed method is sufficiently reliable to become a useful resource for people with modest level of training in statistics. It is also proposed that the method be tabulated for convenient access.

Development of new materials includes establishing relationships between their different components and the physical and chemical properties pertinent to a particular application. In this work, the relationship between several magnetic nanoproperties of cobalt–zinc ferrite as functions of the contents of sodium hydroxide (NaOH) and gadolinium (Gd) are studied using statistically designed experiments. The results point towards the possibility of optimizing the material’s coercivity while leaving the rest of the properties statistically unaffected. This study helps to establish the material’s feasibility to be used in magneto-caloric applications.

Characterization and optimization of material properties during synthesis and processing are frequent problems in materials science and engineering. In the present paper, the use of design of experiments (DOE) to characterize and improve coercivity in cobalt ferrite nanoparticles is described. The sequence of experiments was planned to capitalize on previous experimental runs available. Emphasis is placed on the rationale for choosing the additional runs. The results reveal that the choice of an adequate initial experimental design can save effort and resources in arriving at useful conclusions upon which subsequent experimental runs can be planned, capitalizing on the initial experimental structure. A region with attractive solutions in terms of coercivity, including a local maximum, was identified through the experimental sequences described in this case study.

In this work, a series of novel formulations for a commercial territory design problem motivated by a real-world case are proposed. The problem consists on determining a partition of a set of units located in a territory that meets multiple criteria such as compactness, connectivity, and balance in terms of customers and product demand. Thus far, different versions of this problem have been approached with heuristics due to its NP-completeness. The proposed formulations are integer quadratic programming models that involve a smaller number of variables than heretofore required. These models have also enabled the development of an exact solution framework, the first ever derived for this problem, that is based on branch and bound and a cut generation strategy. The proposed method is empirically evaluated using several instances of the new quadratic models as well as of the existing linear models. The results show that the quadratic models allow solving larger instances than the linear counterparts. The former were also observed to require fewer iterations of the exact method to converge. Based on these results the combination of the quadratic formulation and the exact method are recommended to approach problem instances associated with medium-sized cities.

In this correspondence, a case study involving statistical characterization and multiple criteria optimization on injection molding is presented. This case study is the first application of a method previously described in the literature involving data envelopment analysis geared toward setting design and process variables to meet several performance measures.

Sheet molding compound (SMC) is a fiber-reinforced polymeric composite. It is often used in automotive, marine, and industrial applications over other materials because of its high strength to density ratio, resistance to corrosion, and low cost. There is a demand in the SMC industry to be able to characterize SMC processability. This is particularly true for heavy truck body panels, one of the fastest growing applications of SMC. Because of their large size and high strength requirement, the molding forces have a major influence in the molding cycle. Also because of the long flow paths involved, the ability of the paste to carry glass needs to be properly characterized when developing new SMC materials. In this article, we demonstrate the benefits of using spiral flow as a processability tester. © 2008 Wiley Periodicals, Inc. J Appl Polym Sci, 2008

Injection molding (IM) is the most important process for mass-producing of plastic products. The difficulty of optimizing an IM process is that the performance measures (PMs) usually show conflicting behavior. The aim of this work is to demonstrate a method utilizing CAE, statistical testing, artificial neural networks (ANNs), and data envelopment analysis (DEA) to find the best compromises between multiple PMs, considering the variability in these PMs in an explicit manner. Two case studies are presented. The first case study, based on a virtual part, is discussed in detail in order to illustrate this method. The second case study is experimentally based and makes use of the American Society of Testing Materials (ASTM) mold to illustrate how this approach applies when purely experimental results are available. POLYM. ENG. SCI., 47:400–409, 2007. © 2007 Society of Plastics Engineers

Fiber reinforced structural composites will play a key role in the development of the next generation of transportation vehicles (passenger cars, vans, light trucks, and heavy trucks) because of their high strength-to-weight and stiffness-to-weight ratio compared with metals. An integrated assessment of the durability, reliability, and affordability of these materials is critical to facilitate the inclusion of these materials into new designs. The result of this assessment should provide information to find the balance between the three performance measures. This paper describes a method to develop this assessment in the fabrication of sheet molding compound (SMC) parts, and discusses the concept of Preform Insert Assembly (PIA) for improved affordability in the manufacturing of composite parts. POLYM. COMPOS., 28:233–240, 2007. © 2007 Society of Plastics Engineers

Establishing how a series of potentially important genes might relate to each other is relevant to understand the origin and evolution of illnesses, such as cancer. High-throughput biological experiments have played a critical role in providing information in this regard. A special challenge, however, is that of trying to conciliate information from separate microarray experiments to build a potential genetic signaling path. This work proposes a two-step analysis pipeline, based on optimization, to approach meta-analysis aiming to build a proxy for a genetic signaling path.

The automotive industry is extremely cost sensitive. This is one of the main reasons why sheet molding compound (SMC) compression molding is the most popular fiber-reinforced polymeric composites manufacturing method in this industry. SMC compression molding economics are better suited for the automotive industry than processes such as resin transfer molding or any of its variations. For automotive SMC molders to take advantage of the added stiffness provided by carbon fibers, as an alternative to the widely used glass fibers, the manufacturing process needs to be simplified as much as possible. In actual manufacturing of SMC it is not easy to combine glass fibers with carbon fibers; it will be a lot more convenient to combine SMC plies only with carbon fibers together with SMC plies with only glass fibers. This will also allow molders that will normally use only glass fibers-based SMC to secure carbon fiber SMC from a separate supplier and use it only where it makes economic sense. The main purpose of this study is to investigate the relative improvement in physical properties that can be achieved by substituting glass fibers by carbon fibers in a per ply basis. POLYM. COMPOS. 27:718–722, 2006. © 2006 Society of Plastics Engineers