The application of nanotechnology in different areas of food packaging is an emerging field that will grow rapidly in the coming years. Advances in food safety have yielded promising results leading to the development of intelligent packaging (IP). By these containers, it is possible to monitor and provide information of the condition of food, packaging, or the environment. This article describes the role of the different concepts of intelligent packaging. It is possible that this new technology could reach enhancing food safety, improving pathogen detection time, and controlling the quality of food and packaging throughout the supply chain.

Predicting copper price is essential for making decisions that can affect companies and governments dependent on the copper mining industry. Copper prices follow a time series that is nonlinear and non-stationary, and that has periods that change as a result of potential growth, cyclical fluctuation and errors. Sometimes, the trend and cyclical components together are referred to as a trend-cycle. In order to make predictions, it is necessary to consider the different characteristics of a trend-cycle. In this paper, we study a copper price prediction method using support vector regression (SVR). This work explores the potential of the SVR with external recurrences to make predictions at 5, 10, 15, 20 and 30 days into the future in the copper closing price at the London Metal Exchange. The best model for each forecast interval is performed using a grid search and balanced cross-validation. In experiments on real data sets, our results obtained indicate that the parameters (C, $\epsilon$, $\gamma$) of the model support vector regression do not differ between the different prediction intervals. Additionally, the amount of preceding values used to make the estimates does not vary according to the predicted interval. Results show that the support vector regression model has a lower prediction error and is more robust. Our results show that the presented model is able to predict copper price volatilities near reality, as the root-mean-square error (RMSE) was equal to or less than the $2.2\%$ for prediction periods of 5 and 10 days.

Blockchain technology (BC) offers an innovation platform for decentralized and transparent transactions in the maritime port industry. This technology allows guaranteeing trust, transparency and traceability of cargo and data to be tracked. Today, in the port systems of emerging market countries, BC technology is increasingly being incorporated into information and communication processes. In parallel, the social domain has begun to be explored due to the lack of link between the port and the city it occupies, and the need to incorporate public actors in decision-making at the governance level. In this sense, the present work aims to promote BC technology in order to transform data and information into useful knowledge for effective decision making, through the use of Crowdsourcing. A Crowdsourcing Blockchain CrowdBC conceptual framework and its architecture are generated for a port system in which the cyber-technological, social and cognitive domains (CSTC) of smart ports, the knowledge generation process and Crowdsourcing technology are interrelated. Finally, opportunities are discussed for ports that are in permanent development to reduce the gaps with the smart industry. As a discussion, two possible scenarios and recommendations for future implementations that consider the social and cognitive aspects of Industry 4.0 are presented.

The core problem of a multimodal transportation system is integrating various transportation modes into a cohesive, efficient, and user-friendly network. This study introduces a novel centralized load concentration approach for regions facing geographic challenges. The principal aim is improving multimodal transportation systems by mitigating CO₂ emissions and improving operational efficiency. This will significantly reduce high logistics costs and the environmental impact caused by greenhouse gas emissions, particularly in land transportation, aligning with the global sustainable development goals and offering a promising path towards a more sustainable future. The proposed method implicates direct cargo transportation from its origin to the export ports without passing through intermediate centers. The mathematical model determines the most efficient means of transportation for each route, considering variables such as distance, volume, and type of cargo. Research results indicate that multiple hubs may not be necessary in scenarios with high freight concentration, which could streamline transportation and logistics operations. The modal preferences vary depending on regional dynamics and cargo characteristics, with rail and sea transport emerging as preferable options in specific circumstances, outperforming road transport. The proposed model shows reductions in logistics costs and CO₂ emissions compared to road-focused scenarios. This study provides an adaptable framework for optimizing multimodal transportation systems in regions with similar geographic and logistical attributes. It offers a versatile solution to various contexts and needs. Lastly, the strategic integration of multiple modes of transportation is fundamental to improving efficiency and sustainability.

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.

This note analyzes the effects forest fires in Chile have on vegetation and subsequent ecological restoration. We analyze why forest fires have been a main factor that affects the environment and causes the ecosystem to deteriorate, leading to loss of native forests, species extinction, damage to the urban population, and others. The data examined are derived from fire hotspots in Chile’s central and central-south zones ( ${33}^{\circ }$ 00 ${}^{\prime }$ S– ${41}^{\circ }$ 57 ${}^{\prime }$ S) between 1985 and 2017. We also analyze some key aspects for restoration priorities such as studying affected areas and posterior consequences. Finally, we evaluate actions the country has already taken, and propose further appropriate preventive and restoration measures.