Showing papers by "Instituto Tecnológico Autónomo de México published in 2015"

What Is Different about the Profile of the Social Entrepreneur

Abstract: In recent years, the term social entrepreneur has become increasingly common in academic and business circles. Social entrepreneurs engage in a variety of activities, but always with the intention of solving social problems. Social entrepreneurs are not merely people who perform acts of charity; they have an evident desire to improve social well-being and develop projects with long-term vision. The creation of sustainable social value is a key characteristic that differentiates them from well-meaning individuals who simply engage in charitable works. There are, however, significant gaps in our understanding of social entrepreneurs and few empirical studies on the subject. This present study attempts to identify the characteristics of more socially oriented entrepreneurs, using sociodemographic variables and the theory of universal values toward work. Analysis of a sample of approximately 400 people shows that more than half of entrepreneurial orientation can be explained through the possession of the values of self-enhancement (with an inverse relationship in this case), self-transcendence, and conservation. The theory of universal values has proved extraordinarily useful for studying the characteristics of social entrepreneurs.

Corporate governance and IPO underpricing in a cross-national sample: A multilevel knowledge-based view

Abstract: Prior studies of IPO underpricing, mostly using agency theory and single-country samples, have generally fallen short. In this study, we employ the knowledge-based view (KBV) to explore underpricing across 17 countries. We find that agency indicators are insignificant predictors, board of director knowledge limits underpricing, and external knowledge both substitutes for and complements internal board knowledge. This third finding suggests that future KBV studies should consider how internal and external knowledge states interact with each other. Our study offers new insights into the antecedents of underpricing and extends our understanding of comparative governance and the KBV of the firm.

Does Realized Skewness Predict the Cross-Section of Equity Returns?

Abstract: We use intraday data to compute weekly realized variance, skewness, and kurtosis for equity returns and study the realized moments' time-series and cross-sectional properties We investigate if this week's realized moments are informative for the cross-section of next week's stock returns We find a very strong negative relationship between realized skewness and next week's stock returns A trading strategy that buys stocks in the lowest realized skewness decile and sells stocks in the highest realized skewness decile generates an average weekly return of 19 basis points with a t-statistic of 370 Our results on realized skewness are robust across a wide variety of implementations, sample periods, portfolio weightings, and firm characteristics, and are not captured by the Fama-French and Carhart factors We find some evidence that the relationship between realized kurtosis and next week's stock returns is positive, but the evidence is not always robust and statistically significant We do not find a strong relationship between realized volatility and next week's stock returns

Plasma control of shock wave configuration in off-design mode of M = 2 inlet

Abstract: The objective of this work was to study the steering effect of a weakly ionized plasma on a supersonic flow structure in a two-dimensional aerodynamic configuration with a three-shock compression ramp in an off-design operational mode. Experiments were performed in wind tunnel T-313 of ITAM SB RAS, with the model air inlet designed for operation at a flow of Mach number M = 2. The inlet was tested at M = 2, 2.5, and 3 and with Re = (25–36) × 106/m and an angle of attack AoA = 0°, 5°, and 8°. For the regulation of the inlet characteristics, a plasma generator with electrical power W pl = 2–10 kW was flush-mounted upstream of the compression ramp. A significant plasma effect on the shock configuration at the inlet and on the flow parameters after air compression is considered. It is shown that the main shock wave angle is controllable by means of the plasma power magnitude and, therefore, can be accurately adjusted to the cowl lip of an inlet with a fixed geometry. An additional plasma effect has been demonstrated through a notable increase in the pressure recovery coefficient in a flowpass extension behind the inlet because of an nearly isentropic pattern of flow compression with the plasma turned on. Numerical simulation brings out the details of 3D distribution of the flow structure and parameters throughout the model at thermal energy deposition in inlet near the compression surfaces. We conclude that the plasma-based technique may be a feasible method for expanding supersonic inlet operational limits.

Collaborative Agents for Distributed Load Management in Cloud Data Centers Using Live Migration of Virtual Machines

Abstract: Load management in cloud data centers must take into account 1) hardware diversity of hosts, 2) heterogeneous user requirements, 3) volatile resource usage profiles of virtual machines (VMs), 4) fluctuating load patterns, and 5) energy consumption. This work proposes distributed problem solving techniques for load management in data centers supported by VM live migration. Collaborative agents are endowed with a load balancing protocol and an energy-aware consolidation protocol to balance and consolidate heterogeneous loads in a distributed manner while reducing energy consumption costs. Agents are provided with 1) policies for deciding when to migrate VMs, 2) a set of heuristics for selecting the VMs to be migrated, 3) a set of host selection heuristics for determining where to migrate VMs, and 4) policies for determining when to turn off/on hosts. This paper also proposes a novel load balancing heuristic that migrates the VMs causing the largest resource usage imbalance from overloaded hosts to underutilized hosts whose resource usage imbalances are reduced the most by hosting the VMs. Empirical results show that agents adopting the distributed problem solving techniques are efficient and effective in balancing data centers, consolidating heterogeneous loads, and carrying out energy-aware server consolidation.

Configurations of Capacity for Change in Entrepreneurial Threshold Firms: Imprinting and Strategic Choice Perspectives

Abstract: Imprinting theory suggests that founding conditions are ‘stamped’ on organizations, and these imprinted routines often resist change. In contrast, strategic choice theory suggests that the firm can overcome organizational inertia and deliberately choose its future. Both theories offer dramatically different explanations behind an organization's capacity for change. IPO firms provide a unique context for exploring how imprinting forces interact with strategic choice factors to address organizational capacity for change as a firm moves from private to public firm status. Juxtaposing imprinting and strategic choice perspectives, we employ fuzzy set analysis to examine the multi-level determinants of organizational capacity for change. Our cross-national data reveal three effective configurations of organizational capacity for change within IPOs, and two ineffective configurations. Our results suggest that the antecedents of organizational capacity for change in entrepreneurial threshold firms are non-linear, interdependent, and equifinal.

Agent-based load balancing in Cloud data centers

Abstract: Cloud data centers are generally composed of heterogeneous commodity servers hosting multiple virtual machines (VMs) with potentially different specifications and fluctuating resource usages. This may cause a resource usage imbalance within servers that may result in performance degradation and violations to service level agreements. This work proposes a collaborative agent-based problem solving technique capable of balancing workloads across commodity, heterogeneous servers by making use of VM live migration. The agents are endowed with (i) migration heuristics to determine which VMs should be migrated and their destination hosts, (ii) migration policies to decide when VMs should be migrated, (iii) VM acceptance policies to determine which VMs should be hosted, and (iv) front-end load balancing heuristics. The results show that agents, through autonomous and dynamic collaboration, can efficiently balance loads in a distributed manner outperforming centralized approaches with a performance comparable to commercial solutions, namely Red Hat, while migrating fewer VMs.

A Clustering Method Based on the Maximum Entropy Principle

Abstract: Clustering is an unsupervised process to determine which unlabeled objects in a set share interesting properties. The objects are grouped into k subsets (clusters) whose elements optimize a proximity measure. Methods based on information theory have proven to be feasible alternatives. They are based on the assumption that a cluster is one subset with the minimal possible degree of "disorder". They attempt to minimize the entropy of each cluster. We propose a clustering method based on the maximum entropy principle. Such a method explores the space of all possible probability distributions of the data to find one that maximizes the entropy subject to extra conditions based on prior information about the clusters. The prior information is based on the assumption that the elements of a cluster are "similar" to each other in accordance with some statistical measure. As a consequence of such a principle, those distributions of high entropy that satisfy the conditions are favored over others. Searching the space to find the optimal distribution of object in the clusters represents a hard combinatorial problem, which disallows the use of traditional optimization techniques. Genetic algorithms are a good alternative to solve this problem. We benchmark our method relative to the best theoretical performance, which is given by the Bayes classifier when data are normally distributed, and a multilayer perceptron network, which offers the best practical performance when data are not normal. In general, a supervised classification method will outperform a non-supervised one, since, in the first case, the elements of the classes are known a priori. In what follows, we show that our method's effectiveness is comparable to a supervised one. This clearly exhibits the superiority of our method.

Distributed nestmate recognition in ants.

Abstract: We propose a distributed model of nestmate recognition, analogous to the one used by the vertebrate immune system, in which colony response results from the diverse reactions of many ants. The model describes how individual behaviour produces colony response to non-nestmates. No single ant knows the odour identity of the colony. Instead, colony identity is defined collectively by all the ants in the colony. Each ant responds to the odour of other ants by reference to its own unique decision boundary, which is a result of its experience of encounters with other ants. Each ant thus recognizes a particular set of chemical profiles as being those of non-nestmates. This model predicts, as experimental results have shown, that the outcome of behavioural assays is likely to be variable, that it depends on the number of ants tested, that response to non-nestmates changes over time and that it changes in response to the experience of individual ants. A distributed system allows a colony to identify non-nestmates without requiring that all individuals have the same complete information and helps to facilitate the tracking of changes in cuticular hydrocarbon profiles, because only a subset of ants must respond to provide an adequate response.

Point-of-Dispensing Location and Capacity Optimization via a Decision Support System

Abstract: Dispensing of mass prophylaxis can be critical to public health during emergency situations and involves complex decisions that must be made in a short period of time. This study presents a model and solution approach for optimizing point-of-dispensing (POD) location and capacity decisions. This approach is part of a decision support system designed to help officials prepare for and respond to public health emergencies. The model selects PODs from a candidate set and suggests how to staff each POD so that average travel and waiting times are minimized. A genetic algorithm (GA) quickly solves the problem based on travel and queuing approximations (QAs) and it has the ability to relax soft constraints when the dispensing goals cannot be met. We show that the proposed approach returns solutions comparable with other systems and it is able to evaluate alternative courses of action when the resources are not sufficient to meet the performance targets

Information and communication technologies and climate change adaptation in Latin America and the Caribbean: a framework for action

Abstract: Despite ongoing interest in deploying information and communication technologies (ICTs) for sustainable development, their use in climate change adaptation remains understudied. Based on the integration of adaptation theory and the existing literature on the use of ICTs in development, we present an analytical model for conceptualizing the contribution of existing ICTs to adaptation, and a framework for evaluating ICT success. We apply the framework to four case studies of ICTs in use for early warning systems and managing extreme events in the Latin American and the Caribbean countries. We propose that existing ICTs can support adaptation through enabling access to critical information for decision-making, coordinating actors and building social capital. ICTs also allow actors to communicate and disseminate their decision experience, thus enhancing opportunities for collective learning and continual improvements in adaptation processes. In this way, ICTs can both communicate the current and potential imp...

Cognitive Computing: A Brief Survey and Open Research Challenges

Abstract: Cognitive computing is a multidisciplinary field of research aiming at devising computational models and decision making mechanisms based on the neurobiological processes of the brain, cognitive sciences, and psychology. The objective of cognitive computational models is to endow computer systems with the faculties of knowing, thinking, and feeling. The major contributions of this survey include (i) giving insights into cognitive computing by listing and describing its definitions, related fields, and terms, (ii) classifying current research on cognitive computing according to its objectives, (iii) presenting a concise review of cognitive computing approaches, and (iv) identifying the open research issues in the area of cognitive computing.

Equity Volatility Term Structures and the Cross-Section of Option Returns

Abstract: The slope of the implied volatility term structure is positively related to future option returns. We rank firms based on the slope of the volatility term structure and analyze the returns for straddle portfolios. Straddle portfolios with high slopes of the volatility term structure outperform straddle portfolios with low slopes by an economically and statistically significant amount. The results are robust to different empirical setups and are not explained by traditional factors, higher-order option factors, or jump risk.

Cheating and Incentives: Learning from a Policy Experiment

Abstract: We use a database generated by a policy intervention that incentivized learning as measured by standardized exams to investigate empirically the relationship between cheating by students and cash incentives to students and teachers. We adapt methods from the education measurement literature to calculate the extent of cheating, and show that cheating is more prevalent under treatments that provide monetary incentives to students (versus no incentives, or incentives only to teachers), both in the sense of a larger number of cheating students per classroom and in the sense of more cheating relations per classroom. We also provide evidence of learning to cheat, with both the number of cheating students per classroom and the average number of cheating relations increasing over the years under treatments that provide monetary incentives to students.

Health inequalities in the European Union: an empirical analysis of the dynamics of regional differences

Abstract: In a panel setting, we analyse the speed of (beta) convergence of (cause-specific) mortality and life expectancy at birth in EU countries between 1995 and 2009. Our contribution is threefold. First, in contrast to earlier literature, we allow the convergence rate to vary, and thereby uncover significant differences in the speed of convergence across time and regions. Second, we control for spatial correlations across regions. Third, we estimate convergence among regions, rather than countries, and thereby highlight noteworthy variations within a country. Although we find (beta) convergence on average, we also identify significant differences in the catching-up process across both time and regions. Moreover, we use the coefficient of variation to measure the dynamics of dispersion levels of mortality and life expectancy (sigma convergence) and, surprisingly, find no reduction, on average, in dispersion levels. Consequently, if the reduction of dispersion is the ultimate measure of convergence, then, to the best of our knowledge, our study is the first that shows a lack of convergence in health across EU regions.

An empirical study of work fragmentation in software evolution tasks

Abstract: Information workers and software developers are exposed to work fragmentation, an interleaving of activities and interruptions during their normal work day. Small-scale observational studies have shown that this can be detrimental to their work. In this paper, we perform a large-scale study of this phenomenon for the particular case of software developers performing software evolution tasks. Our study is based on several thousands interaction traces collected by Mylyn, for dozens of developers. We observe that work fragmentation is correlated to lower observed productivity at both the macro level (for entire sessions), and at the micro level (around markers of work fragmentation); further, longer activity switches seem to strengthen the effect. These observations are basis for subsequent studies investigating the phenomenon of work fragmentation.

Economic Policy Uncertainty and the Yield Curve

Abstract: We study the impact of economic policy uncertainty on the term structure of nominal interest rates. We develop a general equilibrium model, in which the real side of the economy is driven by government policy uncertainty and the central bank sets money supply endogenously following a Taylor rule. We analyze the impact of government and monetary policy uncertainty on nominal yields, short rates, bond risk premia, and the term structure of bond yield volatility. Our ane yield curve model is able to capture both the shape of the interest rate term structure as well as the hump-shape of bond yield volatilities. Our empirical analysis shows that higher government policy uncertainty leads to a decline in yields and an increase in bond yield volatility, whereas monetary policy uncertainty has no signicant contemporaneous eect on yields nor volatilities. However, it is an important predictor for bond risk premia.

Investigation of the spraying mechanism and combustion of the suspended coal fuel

Abstract: This paper presents the results of the suspended coal fuel spraying with pneumo- mechanical sprayers followed by the fuel combustion in a vortex furnace, as con- tinuation of our previous research. It is shown that, during the spraying, two qualitatively different systems of drops are forming. The first one with the "drops" diameter above 80-100 μm is presented by coal particles, the other - by water-coal drops. Different dynamics of temperature variation of the coal parti- cle and water-coal fuel drops during their combustion is founded. The residence time of the burning particles and water-coal fuel drops in the vortex furnace is proportional to their diameter, which permits to provide their effective burn-off.

An overview of Non-intrusive load monitoring methodologies

Abstract: Load monitoring is essential in every energy consuming system. Traditional load monitoring system, which used to be intrusive in nature require the installation of sensor to every load of interest which makes the system to be costly and time consuming. Nonintrusive load monitoring (NILM) system uses the aggregated measurement at the utility service entry to identify and disaggregate the appliances connected in the building, which means only one set of sensors is required and it does not require entrance into the consumer premises. Having studied much and working in the area, this paper aim to provide a comprehensive review of the state of art of NILM, the different methods applied by researchers so far, before concluding with the future research direction, which include automatic home energy saving using NILM.