The Moderation of Human Characteristics in the Control Mechanisms of Rumours in Social Media: The Case of Food Rumours in China

Social networks are widely used as a fast and ubiquitous information-sharing medium. The mass spread of food rumours has seriously invaded public’s healthy life and impacted food production. It can be argued that the government, companies, and the media have the responsibility to send true anti-rumour messages to reduce panic, and the risks involved in different forms of communication to the public have not been properly assessed. The manuscript develops an empirical analysis model from 683 food anti-rumour cases and 7,967 data of the users with top comments to test the influence of the strength of rumour/anti-rumour on rumour control. Furthermore, dividing the users into three categories, Leaders, Chatters, and General Public, and study the influence of human characteristics on the relationship between the strength of rumour/anti-rumour and rumour control by considering the different human characteristics as moderator variables. The results showed that anti-rumours have a significant positive impact on the control of rumours; the ambiguity of rumours has a significant negative impact on the Positive Comment Index (PCI) in rumour control. Further, the Leaders increased the overall level of PCI, but negatively adjusted the relationship between evidence and PCI; the Chatters and the General Public reduced the overall level of PCI, and Chatters weakened the relationship between the specific type of anti-rumour form and PCI while the General Public enhanced the relationship between the specific type of anti-rumour form and PCI. In the long run, the role of Leaders needs to be further improved, and the importance of the General Public is growing in the food rumour control process.

Acacia dealbata Link. Aboveground Biomass Assessment: Sustainability of Control and Eradication Actions to Reduce Rural Fires Risk

Invasive species are an environmental problem affecting worldwide ecosystems. In the case of Acacia dealbata Link., the negative impacts affect the productivity of the forests due to the competition established with native species while contributing to a significant increment in the available fuel load, increasing the risk of fire. In Portugal, chemical and mechanical methods are mostly used in the control of these species. However, the costs are often unsustainable in the medium term, being abandoned before completing the tasks, allowing the recovery of the invasive species. The establishment of value chains for the biomass resulting from these actions was pointed out by several authors as a solution for the sustainability of the control process, as it contributes to reducing costs. However, the problems in quantifying the biomass availability make it challenging to organize and optimize these actions. This work, which started from a dendrometrical analysis carried out in stands of A. dealbata, created a model to assess woody biomass availability. The model proved to be statistically significant for stands with trees younger than 20 years old. However, the amount of data collected and the configuration of the settlements analyzed do not allow extrapolation of the model presented to older settlements.

Prevention of ECCD triggering explosive bursts in reversed magnetic shear tokamak plasmas for disruption avoidance

The explosive burst excited by neoclassical tearing mode (NTM) is one of the possible candidates of disruptive terminations in reversed magnetic shear (RMS) tokamak plasmas. For the purpose of disruption avoidance, numerical investigations have been implemented on the prevention of explosive burst triggered by the ill-advised application of electron cyclotron current drive (ECCD) in RMS configuration. Under the situation of controlling NTMs by ECCD in RMS tokamak plasmas, a threshold in EC driven current has been found. Below the threshold, not only are the NTM islands not effectively suppressed, but also a deleterious explosive burst could be triggered, which might contribute to the major disruption of tokamak plasmas. In order to prevent this ECCD triggering explosive burst, three control strategies have been attempted in this work and two of them have been recognized to be effective. One is to apply differential poloidal plasma rotation in the proximity of outer rational surface during the ECCD control process; The other is to apply two ECCDs to control NTM islands on both rational surfaces at the same time. In the former strategy, the threshold is diminished due to the modification of classical TM index. In the latter strategy, the prevention is accomplished as a consequence of the reduction of the coupling strength between the two rational surfaces via the stabilization of inner islands. Moreover, the physical mechanism behind the excitation of the explosive burst and the control processes by different control strategies have all been discussed in detail.

Toward Optimal Control of a Multivariable Magnetic Levitation System

In the paper, a comparative case study covering different control strategies of unstable and nonlinear magnetic levitation process is investigated. Three control procedures are examined in order to fulfill the specified performance indices. Thus, a dedicated PD regulator along with the hybrid fuzzy logic PID one as well as feed-forward neural network regulator are respected and summarized according to generally understood tuning techniques. It should be emphasized that the second PID controller is strictly derived from both arbitrary chosen membership functions and those ones selected through the genetic algorithm mechanism. Simulation examples have successfully confirmed the correctness of obtained results, especially in terms of entire control process quality of the magnetic levitation system. It has been observed that the artificial-intelligence-originated approaches have outperformed the classical one in the context of control accuracy and control speed properties in contrary to the energy-saving behavior whereby the conventional method has become a leader. The feature-related compromise, which has never been seen before, along with other crucial peculiarities, is effectively discussed within this paper.

Characterizing the Effect of Processing Parameters on Temperature Distribution of 7075 Aluminum Alloy Slurry Prepared by Enthalpy Control Process

There is a strong demand for high-strength aluminum alloys such as 7075 aluminum alloy to be applied for rheocasting industry. The overriding challenge for the application of 7075 alloy is that its solid fraction is very sensitive to the variation of temperature in the range of 40% ~ 50% solid fraction, which inevitably narrows down the processing window of slurry preparation for rheocasting process. Therefore, in this work, a novel method to prepare semi-solid slurry of the 7075 alloy, so called Enthalpy Control Process (ECP), has been developed to grapple with this issue. In the method, a medium-frequency electromagnetic field was applied on the outside of slurry preparation crucible to reduce the temperature difference throughout the slurry. The effect of processing parameters, including heating power, heating time, the initial temperature of crucible and melt weight, on the temperature field of the semi-solid slurry was investigated. The results exhibited that although the all the processing parameters had a great influence on the average temperature of the slurry, heating time was the main factor affecting the maximum temperature difference of the slurry. The optimum processing parameters during ECP were found to be heating power of 7.5 KW, the initial temperature of crucible of 30 °C ~ 200 °C and melt weight of 2 kg.

24 Sectors DTC Control with Fuzzy Hysteresis Comparators for DFIM Fed by the Three-level NPC Inverter

This article presents 24 sectors direct torque control (DTC) with fuzzy hysteresis comparators for the doubly-fed induction motor (DFIM) using a three-level neutral point clamped (NPC) inverter. The designed DTC technique of the DFIM combines the advantages of the DTC strategy and fuzzy logic controller. The reaching conditions, stability, and robustness of the DFIM with the designed DTC technique are guaranteed. The designed DTC technique is insensitive to uncertainties, including parameter variations and external disturbances in the whole control process. Finally, the designed DTC technique with fuzzy hysteresis comparators is used to regulate the electromagnetic torque and the flux of the DFIM fed by the three-level NPC inverter and confirms the validity of the designed DTC technique. Results of simulations containing tests of robustness and tracking tests are presented.

Implementation of Load Control for Smart Metering in Smart Grids

The conventional energy meters are not suitable for long operating purposes as they spend much human and material resources. Smart meters, on the other hand, are devices that perform advanced functions including electrical energy consumption recording of residential/industrial users, billing, real-time monitoring, and load balancing. In this chapter, a smart home prototype is designed and implemented. Appliances are powered by the grid during daytime, and a photovoltaic panel stored power during the night or in case of an electricity outage. Second, consumed power from both sources is sensed and further processed for cumulative energy, cost calculations and bill establishment for different proposed scenarios using LABVIEW software. Data are communicated using a USB data acquisition card (DAQ-USB 6008). Finally, a simulation framework using LABVIEW software models four houses each equipped with various appliances. The simulator predicts different power consumption profiles to seek of peak-demand reduction through a load control process.

Artificial Intelligence in Business Processes

The aim of this chapter is to establish that the principles used by neural networks can be applied to business process management. The similarity between artificial neurons and business processes, and hence between neural networks and process landscapes, will be demonstrated. This novel approach leads to an emphasis on process interactions and their effect on actions as a major governing factor in controlling process outputs. Stigmergic interaction in biological systems is explored in the context of business processes, and its potential to understand process interaction is investigated. In order to verify the use of stigmergy in business environments, a pilot study is described in which shop floor business processes in a retailing environment are observed and described using a stigmergic framework. Establishing the viability of using stigmergic interaction to control process actions and outputs is the first step towards designing neural process networks.

Application of Machine Learning Techniques in Additive Manufacturing: A Review

This chapter provides an analysis of the state-of-the-art in ML applications for optimizing the additive manufacturing process. This chapter primarily presents a review of the literature on the use of machine learning (ML) in optimizing the additive manufacturing process at various stages. The chapter identifies ML-researched areas in which ML can be used to optimize processes such as process design, process plan and control, process monitoring, quality enhancement of additively manufactured products, and so on. In addition, general literature on the intersection of additive manufacturing and machine learning will be presented. The benefits and drawbacks of ML for additive manufacturing will be discussed, as well as existing obstacles that are currently limiting applications.

Forecasting Shoe Sales Volume with Exponential Smoothing Method at PT Kujang Utama Antasena - Bogor

PT Kujang Utama Antasena is a shoe industry company specifically for security. The purpose of this study is to forecast or predict sales. This study uses a quantitative method with exponential smoothing, smoothing factor/constant (?) of 0.2. In production activities, forecasting is carried out to determine the amount of demand for a product and is the first step of the production planning and control process to reduce uncertainty so that an estimate that is close to the actual situation is obtained. The exponential smoothing method is a moving average forecasting method that gives exponential or graded weights to the latest data so that the latest data will get a greater weight. In other words, the newer or more current the data, the greater the weight.