Intention to Adopt Industry 4.0 by Organizations in Colombia, Ecuador, Mexico, Panama, and Peru
This study aims to understand the factors that drive actors belonging to the sector of organizations in Latin America (LA) to adopt Industry 4.0. The proposed model results from the analysis and integration of the technology adoption model (TAM), green information technology adoption model (GITAM), and theory of planned behavior (TPB). To determine the predictive factors for internal organizational actors, the research team surveyed information on organizations belonging to Colombia, Ecuador, Mexico, Panama, and Peru. Information was collected from strategic, tactical, and operational personnel. Data were collected from 499 organizational actors in the productive sector, processed, and analyzed using a structural equation model with the partial least squares technique. The study model explains, first there is an influence of the variables Industry 4.0 perceived ease of use (PEU) and Industry 4.0 perceived utility (PUT) on Industry 4.0 attitude towards use (ATU). Second, there is a positive influence of Industry 4.0 technological context (ICO), Industry 4.0 subjective norm (SNO), Industry 4.0 attitude (ATT), Industry 4.0 attitude towards to use (ATU), and Industry 4.0 attitude behavioral control (BCO) on intention to adopt Industry 4.0 in the organization (IAI). Third, what was not supported is the influence of Industry 4.0 technological context (ICO) on the intention to adopt Industry 4.0 in the organization (IAI). The model results are consistent with those of other studies on technology adoption, and propose a model for Industry 4.0, which is a significant contribution to this study, especially for developing countries.
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Induced Overvoltage Caused by Indirect Lightning Strikes in Large Photovoltaic Power Plants and Effective Attenuation Techniques
Indirect Lightning Stroke (ILS) is considered an urgent issue on overall power systems due to its sudden dangerous occurrence. A grid-connected solar Photovoltaic (PV) power plant of 1MW was considered and analyzed using PSCAD/EMTDC software. The effect of grounding grid resistance (
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Low Frequency Magnetic Metasurface for Wireless Power Transfer Applications: Reducing Losses Effect and Optimizing Loading Condition
In this paper, we analyze the operative limits and the conditions characterizing low frequency magnetic metasurfaces used in practical Wireless Power Transfer applications. In the literature, the analytical modeling of similar structures, allowing a control over their frequency response, has been already demonstrated. By starting from this point, practical aspects and differences between a purely theorical approach and real scenario constraints are highlighted and discussed. In particular, accurate and representative numerical simulations are conceived, by firstly analyzing and quantifying the effect on the metasurface performance of the resistive losses characterizing its constitutive conductive material. Then, the metasurface performance deviations produced by the interactions with additional radiofrequency coils, as in Wireless Power Transfer arrangements, are considered. In the latter case, the loading condition at the receiving coil was specifically studied. For both the considered scenarios, detailed conditions and possible solutions to alleviate these detrimental effects, which deviate the metasurface response from the desired behavior, are derived. This work can be useful to guide the design process of low frequency magnetic metasurfaces in a practical environment, achieving the best performance possible for Wireless Power Transfer applications.
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Artificial Intelligence in Education: A Review
The purpose of this study was to assess the impact of Artificial Intelligence (AI) on education. Premised on a narrative and framework for assessing AI identified from a preliminary analysis, the scope of the study was limited to the application and effects of AI in administration, instruction, and learning. A qualitative research approach, leveraging the use of literature review as a research design and approach was used and effectively facilitated the realization of the study purpose. Artificial intelligence is a field of study and the resulting innovations and developments that have culminated in computers, machines, and other artifacts having human-like intelligence characterized by cognitive abilities, learning, adaptability, and decision-making capabilities. The study ascertained that AI has extensively been adopted and used in education, particularly by education institutions, in different forms. AI initially took the form of computer and computer related technologies, transitioning to web-based and online intelligent education systems, and ultimately with the use of embedded computer systems, together with other technologies, the use of humanoid robots and web-based chatbots to perform instructors’ duties and functions independently or with instructors. Using these platforms, instructors have been able to perform different administrative functions, such as reviewing and grading students’ assignments more effectively and efficiently, and achieve higher quality in their teaching activities. On the other hand, because the systems leverage machine learning and adaptability, curriculum and content has been customized and personalized in line with students’ needs, which has fostered uptake and retention, thereby improving learners experience and overall quality of learning.
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Research on Pattern Matching of Dynamic Sustainable Procurement Decision-Making for Agricultural Machinery Equipment Parts
With the digital transformation of the manufacturing industry and the diversification of production methods of agricultural machinery and equipment, external purchase, external coordination, and self-made products continue to increase. If agricultural machinery manufacturing enterprises want to maintain maximum benefits in the fierce competition, they must pay attention to the collaborative procurement decision-making model and the relationship between production, supply, and marketing, and seek a comprehensive dynamic sustainable procurement strategy under the supply chain environment. In this paper, from the manufacturers of agricultural machinery manufacturing enterprises, firstly, three procurement strategies based on line-side inventory supply, third-party logistics supply, and dynamic sustainable supply are studied respectively, while a system dynamics model of collaborative procurement strategy for the agricultural machinery supply chain is constructed and the three procurement strategy models are simulated and analyzed Secondly, the simulation results are analyzed to establish the measurement indexes for evaluating sustainable procurement model matching, and a procurement model matching measurement model based on the topological superiority of object elements combined with the topological hierarchical analysis method and CRITIC comprehensive assignment method is proposed to determine the index weights. And using the correlation function calculation, we get the comprehensive superiority ranking of procurement patterns and the correlation comparison of individual indicators and output the optimal procurement matching pattern and pattern recognition degree. Finally, an application example is given to verify the correctness and practicability of the proposed decision-making model, to provide a qualitative and quantitative dynamic sustainable procurement multi-attribute decision-making tool for the procurement management of agricultural machinery equipment manufacturing enterprises.
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Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
At the dawn of the fourth industrial revolution, we are witnessing a fast and widespread adoption of artificial intelligence (AI) in our daily life, which contributes to accelerating the shift towards a more algorithmic society. However, even with such unprecedented advancements, a key impediment to the use of AI-based systems is that they often lack transparency. Indeed, the black-box nature of these systems allows powerful predictions, but it cannot be directly explained. This issue has triggered a new debate on explainable AI (XAI). A research field holds substantial promise for improving trust and transparency of AI-based systems. It is recognized as the sine qua non for AI to continue making steady progress without disruption. This survey provides an entry point for interested researchers and practitioners to learn key aspects of the young and rapidly growing body of research related to XAI. Through the lens of the literature, we review the existing approaches regarding the topic, discuss trends surrounding its sphere, and present major research trajectories.
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A Review on Microgrids’ Challenges & Perspectives
Due to the sheer global energy crisis, concerns about fuel exhaustion, electricity shortages, and global warming are becoming increasingly severe. Solar and wind energy, which are clean and renewable, provide solutions to these problems through distributed generators. Microgrids, as an essential interface to connect the power produced by renewable energy resources-based distributed generators to the power system, have become a research hotspot. Modern research in the field of microgrids has focused on the integration of microgrid technology at the load level. Due to the complexity of protection and control of multiple interconnected distributed generators, the traditional power grids are now outmoded. Microgrids are feasible alternatives to the conventional grid since they provide an integrating platform for micro-resources-based distributed generators, storage equipment, loads, and voltage source converters at the user end, all within a compact footprint. A microgrid can be architected to function either in grid-connected or standalone mode, depending upon the generation, integration potential to the main grid, and consumers’ requirements. The amalgamation of distributed energy resources-based microgrids to the conventional power system is giving rise to a new power framework. Nevertheless, the grids’ control, protection, operational stability, and reliability are major concerns. There has yet to be an effective real-time implementation and commercialization of micro-grids. This review article summarizes various concerns associated with microgrids’ technical and economic aspects and challenges, power flow controllers, microgrids’ role in smart grid development, main flaws, and future perspectives.
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Wireless Communications Through Reconfigurable Intelligent Surfaces
The future of mobile communications looks exciting with the potential new use cases and challenging requirements of future 6th generation (6G) and beyond wireless networks. Since the beginning of the modern era of wireless communications, the propagation medium has been perceived as a randomly behaving entity between the transmitter and the receiver, which degrades the quality of the received signal due to the uncontrollable interactions of the transmitted radio waves with the surrounding objects. The recent advent of reconfigurable intelligent surfaces in wireless communications enables, on the other hand, network operators to control the scattering, reflection, and refraction characteristics of the radio waves, by overcoming the negative effects of natural wireless propagation. Recent results have revealed that reconfigurable intelligent surfaces can effectively control the wavefront, e.g., the phase, amplitude, frequency, and even polarization, of the impinging signals without the need of complex decoding, encoding, and radio frequency processing operations. Motivated by the potential of this emerging technology, the present article is aimed to provide the readers with a detailed overview and historical perspective on state-of-the-art solutions, and to elaborate on the fundamental differences with other technologies, the most important open research issues to tackle, and the reasons why the use of reconfigurable intelligent surfaces necessitates to rethink the communication-theoretic models currently employed in wireless networks. This article also explores theoretical performance limits of reconfigurable intelligent surface-assisted communication systems using mathematical techniques and elaborates on the potential use cases of intelligent surfaces in 6G and beyond wireless networks.
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Security Hardening of Intelligent Reflecting Surfaces Against Adversarial Machine Learning Attacks
Next-generation communication networks, also known as NextG or 5G and beyond, are the future data transmission systems that aim to connect a large amount of Internet of Things (IoT) devices, systems, applications, and consumers at high-speed data transmission and low latency. Fortunately, NextG networks can achieve these goals with advanced telecommunication, computing, and Artificial Intelligence (AI) technologies in the last decades and support a wide range of new applications. Among advanced technologies, AI has a significant and unique contribution to achieving these goals for beamforming, channel estimation, and Intelligent Reflecting Surfaces (IRS) applications of 5G and beyond networks. However, the security threats and mitigation for AI-powered applications in NextG networks have not been investigated deeply in academia and industry due to being new and more complicated. This paper focuses on an AI-powered IRS implementation in NextG networks along with its vulnerability against adversarial machine learning attacks. This paper also proposes the defensive distillation mitigation method to defend and improve the robustness of the AI-powered IRS model, i.e., reduce the vulnerability. The results indicate that the defensive distillation mitigation method can significantly improve the robustness of AI-powered models and their performance under an adversarial attack.
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A Hybrid Model-Based Approach on Prognostics for Railway HVAC
Prognostics and health management (PHM) of systems usually depends on appropriate prior knowledge and sufficient condition monitoring (CM) data on critical components’ degradation process to appropriately estimate the remaining useful life (RUL). A failure of complex or critical systems such as heating, ventilation, and air conditioning (HVAC) systems installed in a passenger train carriage may adversely affect people or the environment. Critical systems must meet restrictive regulations and standards, and this usually results in an early replacement of components. Therefore, the CM datasets lack data on advanced stages of degradation, and this has a significant impact on developing robust diagnostics and prognostics processes; therefore, it is difficult to find PHM implemented in HVAC systems. This paper proposes a methodology for implementing a hybrid model-based approach (HyMA) to overcome the limited representativeness of the training dataset for developing a prognostic model. The proposed methodology is evaluated building an HyMA which fuses information from a physics-based model with a deep learning algorithm to implement a prognostics process for a complex and critical system. The physics-based model of the HVAC system is used to generate run-to-failure data. This model is built and validated using information and data on the real asset; the failures are modelled according to expert knowledge and an experimental test to evaluate the behaviour of the HVAC system while working, with the air filter at different levels of degradation. In addition to using the sensors located in the real system, we model virtual sensors to observe parameters related to system components’ health. The run-to-failure datasets generated are normalized and directly used as inputs to a deep convolutional neural network (CNN) for RUL estimation. The effectiveness of the proposed methodology and approach is evaluated on datasets containing the air filter’s run-to-failure data. The experimental results show remarkable accuracy in the RUL estimation, thereby suggesting the proposed HyMA and methodology offer a promising approach for PHM.
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