The reduction of social vulnerability to disaster risk as a process of political management is presented with high criticality in all areas of application, therefore, the article seeks to determine-through heuristic documentation-guidelines related to management of risks in the face of natural and anthropogenic disasters, which allow minimizing the impact and effects on the social and economic spheres, while building resilience in the communities. Sometimes, the political and technological capacities of governance do not assume risk management from a preventive perspective, rather reactive, therefore, from this research a perspective is proposed from four cosmogonic dimensions: risk identification, management and monitoring, mitigation actions and response capabilities in the event of an event. Finally, it is considered that the good practices of public management complemented with the capacities of the business sector promote social resilience to disasters.

Mining sector always comes under severe scrutiny due to their negative impacts towards society and environment. Several studies contributed to reduce these impacts exists in the mining operations, in the development, studies also started to explore various assessment mechanism to understand the mining firm's sustainability impact. Among such assessment strategies, sustainability indicators gained huge momentum in recent years specifically with mining operations. This study considers one such area to focus, sustainability indicator analysis in mining. There are several sustainability indicators were introduced in the literature, which makes the consideration of sustainability indicators as a chaotic process for practitioners. Considering the fact, this study sought to explore the influential sustainability indicator and their corresponding sustainability dimension with the case context of China. As a major global manufacturer, China explores different ways to do a sustainable mining business for their long-term growth and this study could impact on their sustainable development goals roadmap. Different sustainability indicators considering mining were collected from the existing studies and further validated and categorized with expert opinions under their respective dimensions of sustainability (economy, environment, and society). The validated sustainability indicators were evaluated through a multi criteria decision making tool, DEMATEL. The inputs for the analysis were collected from a Chinese mining case company. The results revealed the influential sustainability indicator for Chinese mining sector. By understanding the most and least influential indicators, the Chinese mining practitioners can eliminate the strategies to motivate the least influential indicator and improve the strategies to motivate most influential indicator.

The purpose of this research is the design of a green infrastructure that allows a regional conservation area to revalue the Ventanilla wetlands to promote ecotourism through spaces for the conservation of natural resources, turning it into a tourist attraction. The proposal considers design and construction criteria with adequate technology, biodegradable, and sustainable materials where environmental impact is minimized in this context. The collection of information through field visits and the use of different software for the topographic survey. Results show that the infrastructure design proposed was validated by a survey of potential users of the place, with 75% of the interviewees agreeing with the design proposal that allows interaction and harmony with nature, giving it a landscape value, generating local, national, and international visitors. The value is in the ecosystem services that the landscape provides to the city due to the design and construction criteria with adequate technology, biodegradable, and sustainable materials minimizing the environmental impact and promoting the cultural exchange, preservation, and ecological awareness wetland.

This paper proposes an innovative way to address real cases of production prediction. This approach consists in the decomposition of original time series into time sub-series according to a group of factors in order to generate a predictive model from the partial predictive models of the sub-series. The adjustment of the models is carried out by means of a set of statistic techniques and Automatic Learning. This method was compared to an intuitive method consisting of a direct prediction of time series. The results show that this approach achieves better predictive performance than the direct way, so applying a decomposition method is more appropriate for this problem than non-decomposition.

Traditional forecasting models have been widely used for decision-making in production, finance and energy. Such is the case of the ARIMA models, developed in the 1970s by George Box and Gwilym Jenkins [1], which incorporate characteristics of the past models of the same series, according to their autocorrelation. This work compares advanced statistical methods for determining the demand for electricity in Colombia, including the SARIMA, econometric and Bayesian methods.

In the new global and local scenario, the advent of intelligent distribution networks or Smart Grids allows real-time collection of data on the operating status of the electricity grid. Based on this availability of data, it is feasible and convenient to predict consumption in the short term, from a few hours to a week. The hypothesis of the study is that the method used to present time variables to a prediction system of electricity consumption affects the results.

The process of classification of the raw material, is one of the most important procedures in any tea dryer, being responsible for ensuring a good quality of the final product. Currently, this process in most tea processing companies is usually handled by an expert, who performs the work manually and at his own discretion, which has a number of associated drawbacks. In this work, a solution is proposed that includes the planting, design, development and testing of a prototype that is able to correctly classify photographs corresponding to samples of raw material arrived at a dryer, using intelligence techniques (IA) type supervised for Classification by Artificial Neural Networks and not supervised with K-means Grouping for class preparation. The prototype performed well and is a reliable tool for classifying the raw material slammed into tea dryers.

Increasing population and urbanization motivates the capability of consuming more fashion goods than ever. This push creates more momentum on global companies to focus on clothing sectors. Recent advancements, including globalization and e-commerce, have made this sector as one of the top businesses worldwide. Top clothing brands made several strategies to satisfy the stakeholders to sustain in this hot, profitable business. This results in practicing more sustainable strategies, including corporate social responsibility in their clothing business throughout their operations, including the supply chain. However, most of the developed nations are consumers of textiles, which are produced and processed by any of the developing and under developing nations. Meanwhile, achieving sustainability in clothing business includes promoting sustainability in the whole chain of suppliers. Pressure from developed nations urges developing nations to promote sustainable practices in their operations. Several studies discussed the CSR related strategies in textile sectors but failed to explore their critical success factors based on their region. With this concern, this study attempts to study the critical success factors of CSR in textile industries situated in one of the developing nations, China. This study collected the critical success factors from literature and validated with the field experts; then the same were evaluated with the assistance of Chinese textile case industrial managers. Decision-making trial and evaluation laboratory tool has been used to evaluate the influential critical success factors of CSR to promote CSR through motivating those most influential success factors. These results could help the Chinese textile industrial managers to further extend strong roots on CSR implementation. Finally, this study sheds some light on future opportunities that exist within Chinese contexts with the implementation of CSR.

This paper analyses three aspects of the share market operated by the Lima Stock Exchange: (i) the short-term relationship between the pricing, direction and volume of order flows; (ii) the components of the spread and the equilibrium point of the limit order book per share, and (iii) the pricing, order direction and trading volume dynamic resulting from shocks in the same variables when lagged. The econometric results for intraday data from 2012 show that the short-run dynamic of the most and least liquid shares in the General Index of the Lima Stock Exchange is explained by the direction of order flow, whose price impact is temporary in both cases.