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This study evaluates the efficiency of a swarm intelligence algorithm called marriage in honey-bee optimization (MBO) in solving the single-machine weighted earliness/tardiness problem, a type of NP-hard combinatorial optimization problem. The goal is to find the optimal sequence for completing a set of tasks on a single machine, minimizing the total penalty incurred for tasks being completed too early or too late compared to their deadlines. To achieve this goal, the study adapts the MBO metaheuristic by introducing modifications to optimize the objective function and produce high-quality solutions within reasonable execution times. The novelty of this work lies in the application of MBO to the single-machine weighted earliness/tardiness problem, an approach previously unexplored in this context. MBO was evaluated using the test problem set from Biskup and Feldmann. It achieved an average improvement of 1.03% across 280 problems, surpassing upper bounds in 141 cases (50.35%) and matching or exceeding them in 193 cases (68.93%). In the most constrained problems (h = 0.2 and h = 0.4), the method achieved an average improvement of 3.77%, while for h = 0.6 and h = 0.8, the average error was 1.72%. Compared to other metaheuristics, MBO demonstrated competitiveness, with a maximum error of 1.12%. Overall, MBO exhibited strong competitiveness, delivering significant improvements and high efficiency in the problems studied. [ABSTRACT FROM AUTHOR]

The Covid-19 pandemic has challenged both the scientific community and government authorities in Colombia. Both sectors are collaborating to understand the transmission and spread of the virus and to establish control strategies. This study proposes a biomathematical model with difference equations to analyze the transmission of Covid-19 in Santiago de Cali from March 2020 to June 2022. The results indicate that most of the inhabitants could be positive at some point, but with containment measures, a manageable number of symptomatic cases could be maintained. In addition, the cumulative fatality curve is fitted to the Gompertz model. The method used for parameter fitting or estimation was Gauss-Newton. This approach provides valuable information for decision making and pandemic management in the city. [ABSTRACT FROM AUTHOR]

This study offers an innovative solution to address performance issues in the manufacturing process of garlic salt within a condiment-producing SME. A hybrid Lean/Six Sigma model utilizing a Surface Tension Neural Network (STNN) was implemented to control temperature and relative humidity in real-time. The model follows the Define, Measure, Analyze, Improve, Control (DMAIC) methodology to identify root causes and correlate them with waste. By integrating statistical tools, artificial intelligence, and engineering design principles, alternative solutions were evaluated to minimize waste. This document contributes to existing knowledge by demonstrating the integration of an STNN with the Lean/Six Sigma framework in condiment production, an area with limited empirical research. It underscores the benefits of advanced AI technologies in enhancing traditional process optimization methods. The STNN model achieved 97.31% accuracy for temperature classification and 97.37% for humidity, outperforming a Naive Bayes model, which attained 90% accuracy for both. The results showed a 3.15% increase in yield, saving 39.7 kg of waste per batch. Additionally, a 2.13-point improvement at the Six Sigma level was achieved, reducing defects per million opportunities by 551.722. These improvements resulted in significant cost savings, with a reduction in waste-related losses amounting to USD 1585 per batch. The study demonstrates that incorporating artificial intelligence into the Lean/Six Sigma methodology effectively addresses the limitations of traditional statistical methods. Significant improvements in yield and waste reduction highlight the potential of this approach, enhancing operational efficiency and profitability, and fostering sustainable manufacturing practices critical for SMEs' competitiveness and sustainability in the global market. [ABSTRACT FROM AUTHOR]

Early life stress (ELS) is a risk factor for the development of chronic diseases resulting from functional alterations of organs in the cardiorespiratory and renal systems. This work studied the changes in oxidative stress enzyme activities (EAs) of SOD, CAT, GPX, GR, GST, NOS, MDA, and FRAP in different organs (heart, liver, kidney, adrenal glands (AGs), and pancreas) of male and female Sprague–Dawley rat pups on postnatal day (PN) 15, immediately after basal and acute or chronic stress conditions were accomplished, as follows: basal control (BC; undisturbed maternal pups care), stress control (SC; 3 h maternal separation on PN15), basal maternal separation (BMS; daily 3 h maternal separation on PN 1-14), and stress maternal separation (SMS; daily 3 h maternal separation on PN 1-14 and 3 h maternal separation on PN15). Acute or long-term stress resulted in overall oxidative stress, increase in EA, and reduced antioxidant capacity in these organs. Some different response patterns, due to precedent SMS, were observed in specific organs, especially in the AGs. Acute stress exposure increases the EA, but chronic stress generates a response in the antioxidant system in some of the organs studied and is damped in response to a further challenge. [ABSTRACT FROM AUTHOR]

Climate change has a significant impact on generating forest fires. These fires damage property, interrupt productive processes, reduce employment sources, and generate direct economic losses. Also, fires contribute to climate change, resulting in a negative cycle. Therefore, the effective management of forest fires is of vital importance. This research focuses on the combat and mitigation phase of forest fires, with special emphasis on using helicopters to transport water from nearby reservoirs to the fire site. The location of these reservoirs is key since a greater distance traveled by helicopter means a longer delay in water transport, which favors the spread of the fire. For this reason, this research proposes an optimization model to determine the optimal location of these reservoirs in a territory. The proposed model is illustrated with a case study of the region of Valparaiso, demonstrating its usefulness for management and decision making when locating reservoirs for firefighting. [ABSTRACT FROM AUTHOR]

Countries located in the Pacific Ring of Fire, such as Chile, require robust evacuation plans for tall buildings to manage the ongoing threat of natural disasters. This study presents a methodology for developing evacuation plans by conducting pedestrian movement simulations with agents that have a model of their surroundings. This approach allows us to assess different scenarios and choose the best option based on the specific characteristics of the site. The method combines simulation and data analysis, using the Monte Carlo method to improve emergency evacuations. Initially, Pathfinder software was employed to simulate the evacuation of a tall building. This involved modeling pedestrian movements using a multiagent system. These agents were programmed to behave like real pedestrians and make decisions during evacuation scenarios, providing valuable information. The effectiveness of two evacuation strategies was then evaluated using the simulation data. The proposed methodology was validated using a case study. The simulations showed that the best strategy depends on factors such as the distribution of people, the capacity of the exits, and the time available for evacuation. Finally, the model includes a training process that uses virtual reality technology to improve situational awareness. [ABSTRACT FROM AUTHOR]

Copyright of Acta Scientiae (1517-4492) is the property of Acta Scientiae and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Acta Scientiae (1517-4492) is the property of Acta Scientiae and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Despite growing interest in smart manufacturing, there is little information on how organizations can approach the alignment of strategic processes with Industry 4.0. This study seeks to fill this knowledge gap by developing a framework for the integration of Industry 4.0 techniques and artificial intelligence systems. This framework will serve as a conceptual guide in the digital transformation processes toward Industry 4.0. This study involved a systematic literature review of the important methodological proposals and identification of thematic axes, research topics, strategic objectives, challenges, drivers, technological trends, models, and design architectures. In total, 160 articles were selected (120 were published between 2017 and 2022). The results provide insights into the prospects for strategic alignment in the adoption of Industry 4.0. The conceptualization of the framework shows that Industry 4.0 needs strategic adjustments mainly in seven objectives (business model, change mindset, skills, human resources, service level, ecosystem, interconnection, and absorption capacity) derived from 10 thematic axes and 28 research topics. Understanding the strategic adoption of Industry 4.0 and artificial intelligence is vital for industrial organizations to stay competitive and relevant in a constantly evolving business landscape. [ABSTRACT FROM AUTHOR]

Background: All health overuse implies an unnecessary risk of patients suffering adverse events (AEs). However, this hypothesis has not been corroborated by direct estimates for inappropriate hospital admission (IHA). The objectives of the study were the following: (1) to analyze the association between IHA and the development of subsequent AEs; (2) to explore the distinct clinical and economic implications of AEs subsequent IHA compared to appropriate admissions. Methods: An observational cross-sectional study was conducted on hospitalized patients in May 2019 in a high-complexity hospital in Madrid, Spain. The Appropriateness Evaluation Protocol was used to measure IHA, and the methodologies of the Harvard Medical Practice Study and the European Point Prevalence Survey of Healthcare-associated Infections were used to detect and characterize AEs. The association between IHA and the subsequent. Results: A total of 558 patients in the hospital ward were studied. IHA increased the risk of subsequent occurrence of AEs (OR [95% CI]: 3.54 [1.87 to 6.69], versus appropriate) and doubled the mean AEs per patient (coefficient [95% CI]: 0.19 [0.08 to 0.30] increase, versus appropriate) after adjusting for confounders. IHA was a predictive variable of subsequent AEs and the number of AEs per patient. AEs developed after IHA were associated with scheduled admissions (78.9% of AEs, versus 27.9% after appropriate admissions; p < 0.001). Compared with AEs developed after appropriate admissions, AEs after IHA added 2.4 additional days of stay in the intensive care unit and incurred an extra cost of €166,324.9 for the studied sample. Conclusions: Patients with IHA have a higher risk of subsequent occurrence of AE. Due to the multifactorial nature of AEs, IHA is a possible contributing factor. AEs developed after IHA are associated with scheduled admissions, prolonged ICU stays, and resulted in significant cost overruns. [ABSTRACT FROM AUTHOR]

Copyright of Estudios Pedagogicos (Valdivia) is the property of Estudios Pedagogicos and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The purpose of this study is to summarize the pattern recognition (PR) and deep learning (DL) artificial intelligence methods developed for the management of data in the last six years. The methodology used for the study of documents is a content analysis. For this study, 186 references are considered, from which 120 are selected for the literature review. First, a general introduction to artificial intelligence is presented, in which PR/DL methods are studied and their relevance to data management evaluated. Next, a literature review is provided of the most recent applications of PR/DL, and the capacity of these methods to process large volumes of data is evaluated. The analysis of the literature also reveals the main applications, challenges, approaches, advantages, and disadvantages of using these methods. Moreover, we discuss the main measurement instruments; the methodological contributions by study areas and research domain; and major databases, journals, and countries that contribute to the field of study. Finally, we identify emerging research trends, their limitations, and possible future research paths. [ABSTRACT FROM AUTHOR]

This research is framed in the area of biomathematics and contributes to the epidemiological surveillance entities in Colombia to clarify how breast cancer mortality rate (BCM) is spatially distributed in relation to the forest area index (FA) and circulating vehicle index (CV). In this regard, the World Health Organization has highlighted the scarce generation of knowledge that relates mortality from tumor diseases to environmental factors. Quantitative methods based on geospatial data science are used with cross-sectional information from the 2018 census; it's found that the BCM in Colombia is not spatially randomly distributed, but follows cluster aggregation patterns. Under multivariate modeling methods, the research provides sufficient statistical evidence in terms of not rejecting the hypothesis that if a spatial unit has high FA and low CV, then it has significant advantages in terms of lower BCM. [ABSTRACT FROM AUTHOR]

Due to the difficulty in solving combinatorial optimization problems, it is necessary to improve the performance of the algorithms by improving techniques to deal with complex optimizations. This research addresses the metaheuristics of marriage in honey-bees optimization (MBO) based on the behavior of bees. The current study proposes a technique for solving combinatorial optimization problems within proper computation times. The purpose of this study focuses on the travelling salesperson problem and the application of chaotic methods in important sections of the MBO metaheuristic. Three experiments were conducted to measure the efficiency and quality of the solutions: (1) MBO with chaos to generate initial solutions (MBO2); (2) MBO with chaos in the workers (MBO3); and (3) MBO with chaos to generate initial solutions and the workers (MBO4). The application of chaotic functions in MBO was significantly better at solving the travelling salesperson problem. [ABSTRACT FROM AUTHOR]

In this work, we integrate the concepts of Industry 4.0, smart manufacturing, and sustainable manufacturing in a model that provides a conceptual framework for the study of long-term solutions with a high degree of specialization, according to the specific context of each investigation. This study offers a holistic analysis and evaluation of the main challenges facing the Industry 4.0 concept. We also diagnose the current methodological proposals aimed at solving the challenges of Industry 4.0 and sustainability using a systemic review of the literature from the past 5 years. Firstly, we identify 14 technological trends linked to Industry 4.0. Subsequently, the trends are integrated into the proposed model to identify opportunities, evaluating their relationship with three performance areas. This allows the identification of trends that present the greatest number of opportunities in the context of sustainability. The second stage complements the literature review with a descriptive analysis of the studies and discusses the findings. The study concludes that the identified technological trends positively impact Industry 4.0 challenges, helping to achieve sustainable manufacturing objectives. [ABSTRACT FROM AUTHOR]

The decreasing cost of renewable energy resources and the developments in storage system technologies over recent years have increased the penetration of photovoltaic systems to face the high rise in the electricity load. Likewise, there has also been an increase in the demand for tools that make this integration process in the current power systems profitable. This paper proposes a mathematical model based on the DC optimal power flow equations to find the optimal capacity of the PV panels and batteries for a standalone system or a system supported by the grid, while the investment and the energy required by the grid are minimized. In this regard, five different locations have been used as case studies to measure the influence of the irradiance level on the PV-Battery capacity installed and on the economic indicators such as CAPEX, OPEX, NPV, IRR, and the payback period. Thus, a modified 14-bus system has been used to replicate the grid technical limitations and show that a PV-Battery system connected to the grid could produce 26.9% more savings than a standalone PV-Battery and that a location with irradiance levels over 6.08 (kWh/m2/yr) could reduce the payback period for two years. [ABSTRACT FROM AUTHOR]

Copyright of Texto Livre / Texto Livre: Linguagem e Tecnologia is the property of Revista Texto Livre: Linguagem e Tecnologia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The current nature of innovation projects, which integrate a growing number of stakeholders, is increasing the complexity and scope of the product development process. The understanding of the complex and synergistic dynamics between the entire set of stakeholders requires well-adapted approaches to deal with this complexity since the early stages of a project. The present paper proposes a new methodology, based on the Kano model and needs trade-off methodology for a multi-stakeholders' ecosystem during early design stages, enabling to identify and anticipate the needs of an ecosystem of stakeholders. The resulting aggregated Kano matrix enables a holistic vision of the aggregated stakeholders' needs, and then identify emerging needs of the entire ecosystem. The proposed methodology is illustrated through a case study concerning an innovative project of self-directed learning tool apps for smartphones. [ABSTRACT FROM AUTHOR]

Copyright of Medicina Interna de Mexico is the property of Colegio de Medicina Interna de Mexico and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This article reviews studies on the application of reverse logistics in solid waste from the construction industry. The main objective is to provide a summary of current knowledge and specific areas for future research. In addition, construction, as an economic sector, is in a continuous search for new tools to improve its processes, so this research provides the current situation of the relationship between reverse logistics and solid waste in the industry. The review methodology was content analysis of scientific literature published between 1997 and 2020, and total of 66 articles were used. 73% correspond to research articles, around 13% are case studies, and 12% are literature reviews. Only one of the articles is a survey. In addition, 52% of the works reviewed correspond to solid waste studies and 49% are related to the construction industry; only one publication does not classify in any. The most used keywords for the identification of published works were reverse logistics and supply chain; both terms are frequently related to the process and general management of solid waste and construction. The least used term was the literature review that shows the low number of articles that provide a summary of the proposed topic. Finally, three materials were chosen for the study because they are the most used in construction: metals, bricks, and concrete. 15% of the articles study all three, 18% study only metals, and 63% study other materials or are related to construction and solid waste in general. [ABSTRACT FROM AUTHOR]

The Late Triassic Guichon Creek batholith is a large (~ 1800 km2), composite, zoned batholith that hosts several large porphyry Cu-Mo deposits of the Highland Valley Copper district. The batholith consists of intrusive rocks that range in composition from gabbro to quartz monzonite. Adjacent to the mafic margin of the batholith is the Gump Lake granodiorite to quartz monzonite stock. A new U-Pb zircon age of 218 ± 0.18 for the Gump Lake stock indicates that magmatism in the region began at least seven million years prior to the emplacement of the main Guichon Creek batholith rocks at 211 Ma. Zircons from fifteen samples from the Guichon Creek batholith were analyzed by laser ablation ICP-MS to characterize the magmatic evolution and ore fertility of the batholith. The trace element composition of zircon record early, lower crustal, fractional crystallization followed by five pulses of magma recharge and mixing in an upper-crustal, oxidized, magma chamber as well as degassing of the magmatic-hydrothermal fluids that formed the porphyry copper deposits. Zircons from the early barren rocks have chondrite-normalized Eu/EuN* values of 0.19 to 0.56 and estimated temperatures of 850 to 750 °C. The middle to late intrusions that host porphyry copper mineralization have zircon Eu/EuN* values of 0.30 to 0.74 and slightly lower estimated temperatures of 800 to 600 °C. Late porphyritic stocks and dikes from the mineralized centers contain zircon crystals elevated in Y, Nb, Ta, and REE concentration relative to zircon from the earlier intrusions. This distinct change in zircon composition coincides with the copper mineralization, suggesting that zircon chemistry can be used as a tool to identify the genetic evolution of a crystallizing magma chamber and potential for mineralization. [ABSTRACT FROM AUTHOR]

In recent years, different studies have provided estimates of the prevalence of transsexualism with very diverse results. The purpose of this study was to ascertain the prevalence, incidence, and sex ratio of transsexualism in the autonomous region of Madrid (Spain). A total of 1234 patients who attended from 2007 to the end of 2015 in the only Gender Identity Unit (GIU) in Madrid were analyzed. Sixty-three patients were excluded for various reasons; thus, 1171 could be included: 803 male-to-female (MtF) and 368 female-to-male (FtM) transsexual patients. Transsexualism was diagnosed based on the ICD-10, World Health Organization, 1992, and/or gender identity disorder based on the DSM-IV-TR, American Psychiatric Association, 2000. The demographic statistics were calculated on the basis of the population over 15 years old of Madrid. Based on healthcare demand, the prevalence of transsexualism was 22.1 in 100,000 inhabitants: 31.2 for MtF and 12.9 for FtM, making the MtF/FtM ratio approximately 2.2:1. The incidence rate was 2.5 in 100,000 inhabitants, representing an annual average of 130 demands. Although transsexualism occurs in all countries with different rates of prevalence, in our area, this prevalence was higher than reported from other European countries. We believe that two main circumstances might influence this high prevalence: the easy accessibility and the absence of a waiting list to the GIU, and the permissive social and legal climate and openness of Spain, especially in Madrid. [ABSTRACT FROM AUTHOR]

The objective of this work is to review the literature of the main concepts that lead to determining the strategic approach, creation of strategies, organizational structures, strategy formulation, and strategic evaluation as a guide for the organizational management, taking into account the effects produced by the different types of strategies on the performance of organizations. In this article, the systemic literature review method was used to synthesize the result of multiple investigations and scientific literature. The process of reading and analysis of the literature was carried out through digital search engines with keywords in areas related to the strategic management. This research reveals the lack of scientific literature containing important theoretical concepts that serve the strategists as a guide in the creation, formulation, and evaluation of strategies. This review contributes to the existing literature by examining the impact of the strategic management on the organizational performance. [ABSTRACT FROM AUTHOR]

There is a strong emergence of new spaces to foster innovation all over the world (Fablabs, Living Labs and Design Factories, among others). Past experiences have shown these types of projects involving "innovation laboratories" are at risk of not succeeding in their goals. Although several studies have tackled the problem of design, development and sustainability of innovation laboratories, there is still a gap in understanding how the capabilities and performance of these environments are affected by the strategic intentions at the early stages of design and functioning. Throughout this work, eight frameworks from the literature that analyze innovation laboratories are identified and compared. Then, based on both literature and the authors' experience, an updated framework is proposed as the basis for a guidance tool for researchers and practitioners aiming to assess or to adapt an existing project. As part of the operationalization process of the framework, a maturity grid‐based assessment tool is proposed to evaluate the maturity degree of an innovation laboratory. Afterwards, to evaluate the viability and to integrate the perception of innovation laboratory managers, an exploratory study with answers from fifteen laboratories from five different countries is performed. Insights and implications for emerging and already existent projects of innovation laboratories are then discussed. [ABSTRACT FROM AUTHOR]

This work proposes a new edge about the Chaotic Genetic Algorithm (CGA) and the importance of the entropy in the initial population. Inspired by chaos theory, the CGA uses chaotic maps to modify the stochastic parameters of Genetic Algorithm. The algorithm modifies the parameters of the initial population using chaotic series and then analyzes the entropy of such population. This strategy exhibits the relationship between entropy and performance optimization in complex search spaces. Our study includes the optimization of nine benchmark functions using eight different chaotic maps for each of the benchmark functions. The numerical experiment demonstrates a direct relation between entropy and performance of the algorithm. [ABSTRACT FROM AUTHOR]

The study of electrical distribution of primary networks design is oriented to reduce the construction costs and the energy losses by transmission. The topology for the implementation of distribution networks may vary according to the geographical characteristics of the final users and requires specialized optimization solutions with metaheuristics to improve the energy performance of the electrical power systems. A parallel genetic algorithm (PGA) is proposed to optimize a tree-based topology for large-scale electric power distribution networks. The proposed PGA uses the dandelion code, which allows obtaining tree-feasible solutions within each iteration of the PGA. This cannot be achieved with other metaheuristic approaches directly. Eight cores are used simultaneously. We achieve a 22.05% improvement when compared to the tree-feasible solutions obtained with its sequential version. Moreover, the computational time required by the PGA is on average 23 times lower than the sequential version. Finally, we find feasible solutions for instances of the problem with up to 50,000 nodes. [ABSTRACT FROM AUTHOR]

In this article, two models of the forecast of time series obtained from the chaotic dynamic systems are presented: the Lorenz system, the manufacture system, and the volume of the Great Salt Lake of Utah. The theory of the nonlinear dynamic systems indicates the capacity of making good-quality predictions of series coming from dynamic systems with chaotic behavior up to a temporal horizon determined by the inverse of the major Lyapunov exponent. The analysis of the Fourier power spectrum and the calculation of the maximum Lyapunov exponent allow confirming the origin of the series from a chaotic dynamic system. The delay time and the global dimension are employed as parameters in the models of forecast of artificial neuronal networks (ANN) and support vector machine (SVM). This research demonstrates how forecast models built with ANN and SVM have the capacity of making forecasts of good quality, in a superior temporal horizon at the determined interval by the inverse of the maximum Lyapunov exponent or theoretical forecast frontier before deteriorating exponentially. [ABSTRACT FROM AUTHOR]

The dynamic complexity of time series of natural phenomena allowed to improve the performance of the genetic algorithm to optimize the test mathematical functions. The initial populations of stochastic origin of the genetic algorithm were replaced using the series of time of winds and earthquakes. The determinism of the time series brings in more information in the search of the global optimum of the functions, achieving reductions of time and an improvement of the results. The information of the initial populations was measured using the entropy of Shannon and allowed to establish the importance of the entropy in the initial populations and its relation with getting better results. This research establishes a new methodology for using determinism time series to search the best performance of the models of optimization of genetic algorithms (GA). [ABSTRACT FROM AUTHOR]

In this paper, we present the cost of capital estimation for highway concessionaires in Chile. We estimated the cost of equity and the cost of debt and determined the capital structure for each one of twenty-four concessionaires that operate highways. We based our estimations on the developments of Sharpe (1964), Modigliani and Miller (1958), and Maquieira (2009), which were also compared with the Brusov et al. (2015) developments. We collected stock prices for different highway concessionaires around the world from Google Finance and Reuters’ websites in order to determine the Beta of equity using a representative company. After that, we estimated the cost of equity considering Hamada (1969) and a Capital Asset Pricing Model. Then, we estimated the cost of capital using the cost of debt and the capital structure of Chile’s highway concessionaires. With all above, we were able to determine the Weighted Average Cost of Capital (WACC) for highway concessions which ranges from 5.49 to 6.62%. [ABSTRACT FROM AUTHOR]

The present research focuses on the ability to generate a predictive model of the future value of the price of copper using time series of the international price of copper from the last 30 years and the study of chaotic systems. The interest of this study lies in the economic, political and social impacts on a large number of countries with economies based on non-renewable mineral resources. Our approach uses the theoretical foundation of nonlinear dynamic systems for the characterization and analysis of the time series with chaotic component, by using nonlinear tools and methods. Recurrence visual analysis, the Fourier spectrum, the autocorrelation function, mutual information and the Lyapunov exponents are the methods used in the analysis phase of the series, and then a predictive model was generated using nonlinear system techniques. The results identify two cycles of copper prices and price forecasts in the short term. These cycles are connected by a transient and provide further evidence of the effect of investment cycles and infrastructure industries to the value of commodities.&#61472; [ABSTRACT FROM PUBLISHER]

The objective of this work is to make a comparative study of the Marriage in Honeybees Optimization (MBO) metaheuristic for flow-shop scheduling problems. This paper is focused on the design possibilities of the mating flight space shared by queens and drones. The proposed algorithm uses a 2-dimensional torus as an explicit mating space instead of the simulated annealing one in the original MBO. After testing different alternatives with benchmark datasets, the results show that the modeled and implemented metaheuristic is effective to solve flow-shop type problems, providing a new approach to solve other NP-Hard problems. [ABSTRACT FROM AUTHOR]

The stromal vascular fraction (SVF) of adipose tissue is known to contain mesenchymal stem cells (MSC), T regulatory cells, endothelial precursor cells, preadipocytes, as well as anti-inflammatory M2 macrophages. Safety of autologous adipose tissue implantation is supported by extensive use of this procedure in cosmetic surgery, as well as by ongoing studies using in vitro expanded adipose derived MSC. Equine and canine studies demonstrating anti-inflammatory and regenerative effects of non-expanded SVF cells have yielded promising results. Although non-expanded SVF cells have been used successfully in accelerating healing of Crohn's fistulas, to our knowledge clinical use of these cells for systemic immune modulation has not been reported. In this communication we discuss the rationale for use of autologous SVF in treatment of multiple sclerosis and describe our experiences with three patients. Based on this rationale and initial experiences, we propose controlled trials of autologous SVF in various inflammatory conditions. [ABSTRACT FROM AUTHOR]

Copyright of Revista de Geografía Agrícola is the property of Universidad Autonoma Chapingo and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Reports on the Begonia species in Sierra Norte de Puebla, Mexico of which the petiole is used as food. Management applied on the plant; List of Begonia species that are used as quelite; Procedures for the preparation of the petiole.

This study analyses the main challenges, trends, technological approaches, and artificial intelligence methods developed by new researchers and professionals in the field of machine learning, with an emphasis on the most outstanding and relevant works to date. This literature review evaluates the main methodological contributions of artificial intelligence through machine learning. The methodology used to study the documents was content analysis; the basic terminology of the study corresponds to machine learning, artificial intelligence, and big data between the years 2017 and 2021. For this study, we selected 181 references, of which 120 are part of the literature review. The conceptual framework includes 12 categories, four groups, and eight subgroups. The study of data management using AI methodologies presents symmetry in the four machine learning groups: supervised learning, unsupervised learning, semi-supervised learning, and reinforced learning. Furthermore, the artificial intelligence methods with more symmetry in all groups are artificial neural networks, Support Vector Machines, K-means, and Bayesian Methods. Finally, five research avenues are presented to improve the prediction of machine learning. [ABSTRACT FROM AUTHOR]

The research evaluates the vehicular routing problem for distributing refrigerated products. The mathematical model corresponds to the vehicle routing problem with hard time windows and a stochastic service time (VRPTW-ST) model applied in Santiago de Chile. For model optimization, we used tabu search, chaotic search and general algebraic modeling. The model's objective function is to minimize the total distance traveled and the number of vehicles using stochastic waiting restrictions at the customers' facilities. The experiments were implemented in ten scenarios by modifying the number of customers. Experiments were established with several customers that can be solved using the general algebraic modeling technique in order to validate the tabu search and the chaotic search methods. The study considered two algorithms modified with Monte Carlo (tabu search and chaotic search). Additionally, two modified algorithms, TSv2 and CSv2, were proposed to reduce execution time. These algorithms were modified by delaying the Monte Carlo procedure until the first set of sub-optimal routes were found. The results validate the metaheuristic chaotic search to solve the VRPTW-ST. The chaotic search method obtained a superior performance than the tabu search method when solving a real problem in a large city. Finally, the experiments demonstrated a direct relationship between the percentage of customers with stochastic waiting time and the model resolution time. [ABSTRACT FROM AUTHOR]

Leptospirosis is a zoonosis of global distribution, caused by the infection of pathogenic Leptospira, a group of bacteria capable of infecting both domestic and wild animals. Mink (Neovison vison) in southern Chile is recognized as a wild and synanthropic rodent predator (among various other prey), and Leptospira infection in them can be acquired through contact with the pathogen in the environment or by eating infected prey. Thus, the aim of this study was to provide more specifics regarding the source of the infection for the American mink under the conditions of Southern Chile. Minks were captured in the Los Ríos region, southern Chile, in an area with well-developed dairy farming. Two areas were selected for mink trapping, one with a high degree of dairy farming and a second with a low degree of dairy farming. Within them, 16 study sites were visited, and 45 American mink were trapped and euthanized to obtain kidney tissue and blood serum samples for bacteria isolation and determination of antibodies titers, respectively. Molecular characterization of the isolated strains was performed. Three minks from sites of high-dairy farming industry and only one from sites with low-degree dairy farming were detected as infected through molecular confirmation. This study shows evidence that confirms previous findings made in southern Chile, regarding mink as host of Leptospira interrogans serovar Hardjo-prajitno associated to cattle-farming areas. However, typing information (Leptospira interrogans Copenhageni and Icterohaemorrhagiae) suggests that the consumption of rodents may also be a potential source of infection. [ABSTRACT FROM AUTHOR]

This literature review analyzes and classifies methodological contributions that answer the different challenges faced by smart cities. This study identifies city services that require the use of artificial intelligence (AI); which they refer to as AI application areas. These areas are classified and evaluated, taking into account the five proposed domains (government, environment, urban settlements, social assistance, and economy). In this review, 168 relevant studies were identified that make methodological contributions to the development of smart cities and 66 AI application areas, along with the main challenges associated with their implementation. The review methodology was content analysis of scientific literature published between 2013 and 2020. The basic terminology of this study corresponds to AI, the internet of things, and smart cities. In total, 196 references were used. Finally, the methodologies that propose optimization frameworks and analytical frameworks, the type of conceptual research, the literature published in 2018, the urban settlement macro-categories, and the group city monitoring–smart electric grid, make the greater contributions. [ABSTRACT FROM AUTHOR]

This research proposes a new distribution system of goods in the historical center of the city of Santiago, Chile. For the design of the urban logistic system, the methodology city logistics and last mile are used. This design incorporates to the freight transport flexible solutions that improve the efficiency of the distribution process and trade supply, minimizing the environmental impact of the atmospheric pollution (AP). The proposal was made through the data collection, the characterization of the sector and the diagnosis of the urban logistics processes. The analysis of the factors allowed to evaluate the costs of the AP negative externalities. The causes were used as design criteria for the proposals, with the aim of improving the quality of life of the city users. The physical location selection of the Cross-Docking was made through an optimization model of maximum coverage. The optimization algorithm of the nearest neighbor was proposed for vehicle routing. The analytic hierarchy process (AHP) was used to generate a ranking of the best non-polluting vehicles to be used in the zone. Finally, the results obtained allowed a 53 ton decrease in carbon dioxide in the square kilometer and reduced 1103 h of interruptions per year in the vehicular congestion of the sector. [ABSTRACT FROM AUTHOR]

The stromal vascular fraction (SVF) of adipose tissue is known to contain mesenchymal stem cells (MSC), T regulatory cells, endothelial precursor cells, preadipocytes, as well as anti-inflammatory M2 macrophages. Safety of autologous adipose tissue implantation is supported by extensive use of this procedure in cosmetic surgery, as well as by ongoing studies using in vitro expanded adipose derived MSC. Equine and canine studies demonstrating anti-inflammatory and regenerative effects of non-expanded SVF cells have yielded promising results. Although non-expanded SVF cells have been used successfully in accelerating healing of Crohn's fistulas, to our knowledge clinical use of these cells for systemic immune modulation has not been reported. In this communication we discuss the rationale for use of autologous SVF in treatment of multiple sclerosis and describe our experiences with three patients. Based on this rationale and initial experiences, we propose controlled trials of autologous SVF in various inflammatory conditions. [ABSTRACT FROM AUTHOR]