Nixtamal techniques for different maize races prepared as tortillas and tostadas by women of Chiapas, Mexico

Nixtamalization, which means cooking maize in alkaline water, is the central technique for the culinary use of maize in Mexico and Central America. Without this procedure, relying on maize as the basic starch staple is inadequate because of nutriment deficiencies. Mexico has more than 50 principal racial types of maize, and these differ in grain qualities that can require the adjustment of the nixtamalization process. Properties such as hardness and grain composition influence nixtamalization because they are related to the absorption of water that occurs during cooking and steeping. Some maize preparations, like tortillas and tostadas, can also require the adjustment of nixtamalization to obtain a high-quality foodstuff. We studied how women in three regions of the state of Chiapas, which differ in the prevalent maize race available, prepare their nixtamal and whether they make changes according to the type of food they prepare. Interviews of 30 women follow the measure of relevant variables when the women prepared nixtamal. We found that nixtamalization is adjusted for different grain hardnesses and for environmental conditions. Variations were found in the cooking time of the nixtamal, in the amount of time the nixtamal was steeped, and in a special process of double boiling of the nixtamal performed by some women for tostadas. Women that specialize in production for the market have developed variations preferred by consumers. As practiced by women in Chiapas, nixtamalization is a flexible technique that is adjusted for maize type and for food preparations.

Susceptibility of almond (Prunus dulcis) cultivars to twig canker and shoot blight caused by Diaporthe amygdali

Twenty-five almond cultivars were assessed for susceptibility to Diaporthe amygdali, causal agent of twig canker and shoot blight disease. In laboratory experiments, growing twigs were inoculated with four D. amygdali isolates. Moreover, growing shoots of almond cultivars grafted onto INRA ‘GF-677’ rootstock were used in four-year field inoculations with one D. amygdali isolate. In both type of experiments, inoculum consisted of agar plugs with mycelium, which were inserted underneath the bark and the lesion lengths caused by the fungus were measured. Necrotic lesions were observed in the inoculated almond cultivars both in laboratory and field tests, confirming the susceptibility of all the evaluated cultivars to all the inoculated isolates of D. amygdali. Cultivars were grouped as susceptible or very susceptible according to a cluster analysis. The relationship between some agronomic traits and cultivar susceptibility was also investigated. Blooming and ripening times were found relevant variables to explain cultivars performance related to D. amygdali susceptibility. Late and very late blooming, and early and medium ripening cultivars were highly susceptible to D. amygdali. Our results may provide valuable information that could assist in ongoing breeding programs of this crop and additionally in the selection of cultivars for new almond plantations.

A multi-source global-local model for epidemic management

The Effective Reproduction Number Rt provides essential information for the management of an epidemic/pandemic. Projecting Rt into the future could further assist in the management process. This article proposes a methodology based on exposure scenarios to perform such a procedure. The method utilizes a compartmental model and its adequate parametrization; a way to determine suitable parameters for this model in México’s case is detailed. In conjunction with the compartmental model, the projection of Rt permits estimating unobserved variables, such as the size of the asymptomatic population, and projecting into the future other relevant variables, like the active hospitalizations, using scenarios. The uses of the proposed methodologies are exemplified by analyzing the pandemic in a Mexican state; the main quantities derived from the compartmental model, such as the active and total cases, are included in the analysis. This article also presents a national summary based on the methodologies to illustrate how these procedures could be further exploited. The supporting information includes an application of the proposed methods to a metropolitan area to show that it also works well at other demographic disaggregation levels. The procedures developed in this article shed light on how to develop an effective surveillance system when information is incomplete and can be applied in cases other than México’s.

A Pricing Model for Urban Rental Housing Based on Convolutional Neural Networks and Spatial Density: A Case Study of Wuhan, China

With the development of urbanization and the expansion of floating populations, rental housing has become an increasingly common living choice for many people, and housing rental prices have attracted great attention from individuals, enterprises and the government. The housing rental prices are principally estimated based on structural, locational and neighborhood variables, among which the relationships are complicated and can hardly be captured entirely by simple one-dimensional models; in addition, the influence of the geographic objects on the price may vary with the increase in their quantities. However, existing pricing models usually take those structural, locational and neighborhood variables as one-dimensional inputs into neural networks, and often neglect the aggregated effects of geographical objects, which may lead to fluctuating rental price estimations. Therefore, this paper proposes a rental housing price model based on the convolutional neural network (CNN) and the synthetic spatial density of points of interest (POIs). The CNN can efficiently extract the complex characteristics among the relevant variables of housing, and the two-dimensional locational and neighborhood variables, based on the synthetic spatial density, effectively reflect the aggregated effects of the urban facilities on rental housing prices, thereby improving the accuracy of the model. Taking Wuhan, China, as the study area, the proposed method achieves satisfactory and accurate rental price estimations (coefficient of determination (R2) = 0.9097, root mean square error (RMSE) = 3.5126) in comparison with other commonly used pricing models.

Structural time series: computational efficiency in estimating economic parameters in industry

A growing field is related to automatized Time Series analysis, through complicated due to the dependence of observed and hidden dimensions often presented in these data types. In this report the problem is motivated by a Brazilian financial company interested in unraveling relation structure explanation of the Japanese' CPI ex-fresh Food \& Energy across 157 economical exogenous variables, with very limiting data. The problem becomes more complex when considering that each variable can enter the model with lags of 0 to 8 periods, as well as an additional restriction of admitting only a positive relationship. This report discusses three possible treatments involving models for structured time series, the most relevant approach found in this study is a Dynamic Regression Model combined with a Stepwise algorithm, which allows the most relevant variables, as well as their respective lags, to be found and inserted in the model with low computational cost.

Factor Structure of the AUDIM-M Dimensional Self-Concept Questionnaire in Mexican Adolescents

Self-concept is one of the most relevant variables in the field of personality, and a negative self-perception can pose a risk to the adolescent’s development. The present study aimed to analyze the psychometric properties proposed by Aguirre and collaborators for the dimensional self-concept questionnaire (AUDIM-M). The total sample was 560 adolescents from the city of Chihuahua, Chihuahua, with a mean age of 12.96 ± 0.88 years. The factor structure of the questionnaire was analyzed using confirmatory factor analysis. The analyses show that a four-factor structure is viable and adequate (GFI 0.964; RMSEA 0.057; CFI 0.950). The four-factor structure (personal self-concept, physical self-concept, social self-concept, and academic self-concept), according to statistical and substantive criteria, shows adequate indicators of reliability and validity adjustment. The model obtained coincides with that proposed by Aguirre et al. Improving adolescents’ self-concept undoubtedly contributes to their quality of life, hence the need for valid and reliable instruments for its measurement; this study could be a first approach for future research.

Consumer willingness to pay for cheese with a social sustainability attribute

ABSTRACT: The social dimension of sustainability is becoming very relevant on the consumer purchasing decision, especially in the food sector. This research analyzed the willingness to pay (WTP) of Chilean consumers for cheese with a social sustainability attribute using a double-bounded dichotomous choice format. Results showed that the WTP for a price premium depends on three variables: age, income and previous knowledge regarding social sustainability. The mean WTP is 7.5% over the average price of cheese; although, combining relevant variables and changing values over their range shows that firms can obtain up to a 12.9% price premium if they focus on younger and high income consumers that have a previous good knowledge about social sustainability practices. Additionally, this study presented a novel market oriented methodological approach for identifying and quantifying specific niche markets based on the WTP.

Corporate Governance and Firm Innovation

Innovation is a key factor for firms' competitive advantage in the long-term and for their financial success. Scholars highlight the underinvestment problem with respect of R&D investment. This chapter focuses on two relevant variables of corporate governance that influence firms' innovation performance: firm ownership and board of directors. In the first section, the effect of ownership structure on R&D investment is analyzed. More specifically, the chapter will illustrate the effects of family ownership and institutional ownership on innovation investments. The second section explores the main theoretical perspectives investigating the functions of board of directors and the main board tasks. Lastly, three attributes of board structure and their effect on R&D investments are explored.

A machine learning approach to find the determinants of Peruvian coca illegal crops

The current study analyzed the determinants of the Peruvian coca illegal plantations in the period 2003-2019. Hence, the DEVIDA database variables were gathered at first. Then, a machine learning-based technique is employed to select the most relevant variables for the study. That technique, Lasso, selected as accurate variables eradication of coca plantations and pasta base. Both OLS and VAR are employed to analyze the relevance of the selected variables. OLS finds that eradication was negatively related to the dependent variable. Nonetheless, pb confiscation had a positive relationship with illegal coca crops. Furthermore, VAR encounters that only pb confiscation affected the dependent variable. Supplementary tests are carried to ensure the accuracy of the results. In consequence, it is concluded that eradication policies by themselves were not enough to discourage the coca plantations. Farmers should get instruction about alternative crops and financial help. Furthermore, it has been claimed that pb confiscation generates scarcity of the drug, which elevates its price. Thus, coca farmers are more motivated to plant coca because of the higher prices. Therefore, as long as the international demand, which is disposed to pay high prices, the coca illegal crops and its illicit products will exist.

Hypercompetition: a review and agenda for future research

Purpose The purpose of this study is to review and understand the dimensionality of hypercompetition, factors causing a hypercompetitive environment and the consequences of hypercompetition on markets. Thereby, the purpose of the study is to cover the main contributors in the research field of hypercompetition and explore their findings and different views on hypercompetition. Design/methodology/approach Systematically review 131 conceptual and empirical studies published or presented at a conference in the past 25 years, with the focus on the definitions, causes and consequences (or presumed effects) of hypercompetition. In the paper, I follow the well-known systematic literature review method by Tranfield et al. (2003). Findings The contribution of the study is to advance the knowledge of researchers and managers, in such a way that it becomes easier for them to select relevant variables to measure hypercompetition and identify strategies for gaining temporary competitive advantages in hypercompetitive environments. The construct of hypercompetition required a consolidation of commonalities in the definitions and characteristics used by scholars, to ensure that proper assumptions and variables are being used to measure hypercompetition in future research. Several ways to measure the effects of hypercompetition on firms, industries and individuals have been proposed, but the field still lacks of a clear definition on how to directly measure the construct. Practical implications In this paper, I highlight three managerial implications of hypercompetition as follows: action-based strategy, the determinants of hypercompetition and the importance for managers of accurately establishing their firm’s competitive situation. Originality/value Previous reviews in the area have either focussed on specific components or effects of hypercompetition. The present study collectively takes into consideration the definitions, causes and consequences of hypercompetition on firms, industries and individuals. The contribution of the paper is to indicate future opportunities and challenges within research on hypercompetition.