Comparison of a Rapid Light-Induced and Forced Test to Study the Oxidative Stability of White Wines

The oxidation processes of white wines can occur during storage and commercialization due to several factors, and these can negatively affect the color, aroma, and quality of the wine. Wineries should have faster and simpler methods that provide valuable information on oxidation stability of wines and allow fast decision-making procedures, able to trigger suitable technological interventions. Using a portable prototype instrument for light irradiations at different wavelengths and times was considered and evaluated on sensorial, spectrophotometric, and colorimetric parameters of white wines. The sensorial analysis revealed that white and light blue were the most significant, after only 1 h of irradiation. The experimental results showed that hydrogen peroxide could enhance the effect of light treatment, allowing a contemporary evaluation of the oxidation stability of wine against light and chemical stresses. As expected, a good correlation (R2 > 0.89) between optical density at 420 nm and b* parameter was highlighted. The synergic effect of light and H2O2 was also studied on the hydrolyzable and condensed tannins’ additions to white wine. The proposed methodology could be used to evaluate the oxidative stability of white wines, but also to evaluate the effect of some oenological adjuvants on wine stability.

Literature Study on the Use of Big Data and Artificial Intelligence in Policy Making in Indonesia

The use of big data and artificial intelligence in decision-making in Indonesia is still rarely implemented. But in the business world, big data and artificial intelligence are very commonplace to boost targets. This study discusses the use of big data and artificial intelligence in policy Making in Indonesia. The method used in this paper is qualitative research with a literature study approach. The result of this research is that the dynamics in the implementation of public services require appropriate and fast decision making, considering that this is a community demand. Therefore, public leaders need to disrupt themselves in public services so that these services can be served quickly. In conclusion, big data and artificial intelligence can help public leaders make decisions to deliver the best policies. This research implies that it can be used as a reference for policymakers that big data and artificial intelligence can be used in decision-making to warn Policymaking.

Software development in disruptive times

Creating a software solution with fast decision capability, agile project management, and extreme low-code technology.

Geographic Information Systems supported by multi-criteria decision analysis to indicate potentially suitable areas for construction and demolition waste disposal

In Brazil, the disposal of construction and demolition waste (CDW) quite often occurs in inadequate places, resulting in social, economic, and environmental problems. This reflects the need for selecting appropriate areas for the disposal of this type of waste. These areas must follow local standards and regulations to protect human health and the environment. Considering that, this study is intended to indicate potentially suitable areas for CDW landfill deployment, known as Class A landfill in Brazil, supported by a GIS-MCDA based model. The GIS-MCDA technique, used as a basic tool to identify potentially suitable areas, has several advantages, such as low cost, reduced spatial data subjectivity, and fast decision-making process. The place chosen for this study is the Urban Central Core of the Metropolitan Area of Curitiba. By integrating GIS with MCDA techniques in this research study, it was possible to indicate potentially suitable areas for CDW disposal in this region.

Complementary Feature Extractions for Event Identification in Power Systems Using Multi-Channel Convolutional Neural Network

This paper presents an event identification process in complementary feature extractions via convolutional neural network (CNN)-based event classification. The CNN is a suitable deep learning technique for addressing the two-dimensional power system data as it directly derives information from a measurement signal database instead of modeling transient phenomena, where the measured synchrophasor data in the power systems are allocated by time and space domains. The dynamic signatures in phasor measurement unit (PMU) signals are analyzed based on the starting point of the subtransient signals, as well as the fluctuation signature in the transient signal. For fast decision and protective operations, the use of narrow band time window is recommended to reduce the acquisition delay, where a wide time window provides high accuracy due to the use of large amounts of data. In this study, two separate data preprocessing methods and multichannel CNN structures are constructed to provide validation, as well as the fast decision in successive event conditions. The decision result includes information pertaining to various event types and locations based on various time delays for the protective operation. Finally, this work verifies the event identification method through a case study and analyzes the effects of successive events in addition to classification accuracy.

A Novel Fast Decision-making Method Based on Integrated Intelligent Technologies for Wind Disaster Management of Energy Manufacture

Energy manufacture is very important to all of industries. Typhoons hit the power grid in China's southeast coastal areas frequently for the past few years, seriously affecting the industries’ operation. Therefore, making-decision of wind damage management for nation's electricity grid in real time is an urgent subject to be studied. The traditional decision making method is easy to be implemented, but is not proper for dealing with nonlinear problems in complex systems. The purpose of this article is to design a fast decision making framework for accomplishing fast decision making by making combination Case-Based Reasoning (CBR) with Rule-Based Reasoning (RBR), Genetic Algorithm (GA), which is called fast decision making method based on integrated intelligent technologies (FDMMBIIT). Compared with traditional methods, FDMMBIIT completes case adaptation with BPNN after extending case base. To make the decision-making more accurate, this article updated the multi-object genetic algorithm (MOGA) with adaptive genetic operators and a selection method by using the fitness function. Likewise, BPNN is improved with adaptive simulated annealing algorithm (ASAA), which is named as BPNNASAA. More important, this paper expands the frame theory by integrating it to the D/S evidence theory, exploring a novel solution to representing cases with incomplete information. The case of Guangdong demonstrates FDMMBIIT achieves better decision-making performance for storm disaster emergency management.

An intelligent decision-making method for anti-jamming communication based on deep reinforcement learning

In order to solve the problem of intelligent anti-jamming decision-making in battlefield communication, this paper designs an intelligent decision-making method for communication anti-jamming based on deep reinforcement learning. Introducing experience replay and dynamic epsilon mechanism based on PHC under the framework of DQN algorithm, a dynamic epsilon-DQN intelligent decision-making method is proposed. The algorithm can better select the value of epsilon according to the state of the decision network and improve the convergence speed and decision success rate. During the decision-making process, the jamming signals of all communication frequencies are detected, and the results are input into the decision-making algorithm as jamming discriminant information, so that we can effectively avoid being jammed under the condition of no prior jamming information. The experimental results show that the proposed method adapts to various communication models, has a fast decision-making speed, and the average success rate of the convergent algorithm can reach more than 95%, which has a great advantage over the existing decision-making methods.