Showing papers on "Boom published in 2022"

SCADA Data Based Wind Power Interval Prediction Using LUBE-Based Deep Residual Networks

Abstract: Wind is a pollution-free renewable energy source. It has attracted increasing attention owing to the decarbonization of electricity generation. However, owing to the dynamic nature of wind speed, ensuring a stable supply of wind energy to electric grid networks is challenging. Therefore, accurate short-term forecasting of wind power prediction plays a key role for wind farm engineers. With the boom in AI technologies, deep-learning-based forecasting models have demonstrated superior performance in wind power forecasting. This paper proposes a short-term deep-learning-based interval prediction algorithm for forecasting short-term wind power generation in wind farms. The proposed approach combines the lower upper bound estimation (LUBE) method and a deep residual network (DRN). Wind farm data collected in northwestern China are selected for this empirical study. The proposed approach is compared with three benchmark short-term forecasting approaches. Extensive experiments conducted on the data collected from five wind turbines in 2021 indicate that the proposed algorithm is efficient, stable, and reliable.

A striking growth of CO2 emissions from the global cement industry driven by new facilities in emerging countries

Abstract: Global industrialization and urbanization processes enabled a diverse cement production boom over the past three decades, as cement is the most important building construction material. Consequently, the cement industry is the second-largest industrial CO2 emitter (∼25% of global industrial CO2 emissions) globally. In this study, the Global Cement Emission Database, which encompasses anthropogenic CO2 emissions of individual production units worldwide for 1990–2019, was developed. A recently developed unit-level China Cement Emission Database was then applied to override China’s data and the combination of two databases is used to reveal the unit characteristics of CO2 emissions and ages for global cement plants, assess large disparities in national and regional CO2 emissions, growth rates and developmental stages from 1990–2019, and identify key emerging countries of carbon emissions and commitment. This study finds that globally, CO2 emissions from the cement industry have increased from 0.86 Gt in 1990 to 2.46 Gt in 2019 (increasing by 186%). More importantly, the large CO2 emissions and the striking growth rates from those emerging countries, including most of the developing countries in the Asia region and the Middle East and Africa region, are clearly identified. For example, the Middle East and Africa, including mostly developing or underdeveloped countries, only represented 0.07 Gt CO2 in 1990 (8.4% of the total), in contrast to 0.26 Gt (10.4% of the total) CO2 in 2019, which is a 4.5% average growth rate during 1990–2019. Further, the intensive expansion of large and new facilities since 2005 in Asia and the Middle East and Africa has resulted in heavy commitment (90.1% of global commitment in 2019), and mitigation threats in the future considering their increasing emissions (the national annual growth rate can be up to >80%) and growing infrastructure construction (∼50% of clinker capacity operating ⩽10 years). Our results highlight the cement industry’s development and young infrastructure in emerging economies; thus, future increasing cement demand and corresponding carbon commitment would pose great challenges to future decarbonization and climate change mitigation.

Predictable Financial Crises

Abstract: Using historical data on post-war financial crises around the world, we show that the combination of rapid credit and asset price growth over the prior three years, whether in the nonfinancial business or the household sector, is associated with a 40% probability of entering a financial crisis within the next three years. This compares with a roughly 7% probability in normal times, when neither credit nor asset price growth is elevated. Our evidence challenges the view that financial crises are unpredictable “bolts from the sky” and supports the Kindleberger-Minsky view that crises are the byproduct of predictable, boom-bust credit cycles. This predictability favors policies that lean against incipient credit market booms. This article is protected by copyright. All rights reserved

Colonial Financial Infrastructures and Kenya’s Uneven Fintech Boom

Abstract: : Kenya is a widely cited case for proponents of fi ntech for development. This article shows how Kenya ’ s fi ntech boom replicates patterns of uneven development inherited from the colonial era. In particular, fi ntech use is unevenly distributed between urban and rural areas, and heavily concentrated on Nairobi and Mombasa in particular. The article seeks to explain these patterns by situating them in relation to the spatiality and political economy of settler-colonial agriculture, tracing successive (unsuccessful) efforts at reforming the fi nancial system to ameliorate social and spatial disparities inherited from the colonial era. It does so drawing on recent debates about “ fi nancial infrastructures ” , alongside considerations of the political economy of land, property relations, and the state.

A Survey on Artificial Intelligence Aided Internet-of-Things Technologies in Emerging Smart Libraries

Abstract: With the boom in artificial intelligence (AI) and Internet-of-Things (IoT), thousands of smart devices are interconnected with each other and deeply applied into human society. This prosperity has significantly improved public service and management, which were traditionally based on manual work. As a notable scenario, librarianship has embraced an era of “Smart Libraries” enabled by AI and IoT. Unlike existing surveys, our work comprehensively overviews the AI- and IoT-based technologies in three fundamental aspects: smart service, smart sustainability, and smart security. We then further highlight the trend towards future smart libraries.

Technical Job Recommendation System Using APIs and Web Crawling

Abstract: There has been a sudden boom in the technical industry and an increase in the number of good startups. Keeping track of various appropriate job openings in top industry names has become increasingly troublesome. This leads to deadlines and hence important opportunities being missed. Through this research paper, the aim is to automate this process to eliminate this problem. To achieve this, Puppeteer and Representational State Transfer (REST) APIs for web crawling have been used. A hybrid system of Content-Based Filtering and Collaborative Filtering is implemented to recommend these jobs. The intention is to aggregate and recommend appropriate jobs to job seekers, especially in the engineering domain. The entire process of accessing numerous company websites hoping to find a relevant job opening listed on their career portals is simplified. The proposed recommendation system is tested on an array of test cases with a fully functioning user interface in the form of a web application. It has shown satisfactory results, outperforming the existing systems. It thus testifies to the agenda of quality over quantity.

Low-Boom Demonstrator Near-Field Summary for the Third AIAA Sonic Boom Prediction Workshop

Abstract: A summary and statistical analysis of the near-field computational fluid dynamics (CFD) submissions for the Third AIAA Sonic Boom Prediction Workshop is provided with a focus on the C608 Low Boom Flight Test Demonstrator. The C608 is more complex in terms of geometry and propulsion boundary conditions than previous workshop cases and is more representative of vehicles with lower ground loudness and the potential for lower annoyance. The near-field signatures submitted by the participants are propagated to the ground to compute statistics of loudness measures over the vehicle sonic boom carpet. Principle component analysis is used to extract the primary variation modes. Context from previous sonic boom workshops indicates that this workshop has the lowest variation even though the case is more challenging because it is quieter and more complex. The results documented in this summary indicate that the international state-of-the-art for near-field CFD has a variation that is small enough for meaningful low-boom design and analysis. Low-boom configuration analysis methods with low variation are important tools to develop certification processes for addressing the prohibition on overland supersonic commercial flight.

Book review: Yuen Yuen Ang, China’s Gilded Age: The Paradox of Economic Boom and Vast Corruption

Abstract: Yuen Yuen Ang, China’s Gilded Age: The Paradox of Economic Boom and Vast Corruption (Cambridge, MA: Cambridge University Press, 2020). XV + 257 pp., ₹3,512, ISBN: 978-1108478601 (Hardback).

A bibliometric analysis on innovation convergence

Abstract: PurposeInnovation convergence is critical to national or regional economic growth patterns. This article provides a systematic review of innovation convergence research through qualitative discussions combined with bibliometric methods. Through this article, researchers interested in the field of innovation convergence can quickly understand the development of the field, quickly identify authors and publications with significant impact, and collaborative networks in the field.Design/methodology/approachThis article is based on the relevant literature included in the WOS database from 1990 to 2021, using Citespace, Gephi and other software to conduct a systematic bibliometric analysis of the research in the new convergence field.FindingsThis research shows that the second half of the twentieth century was a boom period for research on economic convergence. 2. The subject foundation of innovation convergence research mainly includes mathematics, economics, political science and computational science. 3. The journals that publish research in this field are widely distributed, including the fields of economics, natural sciences and complex sciences. 4. The research in the field of innovation convergence is inseparable from the research in the field of economic growth.Originality/valueThis study may help others to understand the development history and research trends of the innovation convergence field, as well as the literature and cooperative scientific research institutions that have an important influence.

Targeted Allocation of Marketing Resource in Networks Based on Opinion Dynamics

Abstract: Recent advances in information technology and the boom in social media provide firms with easy access to the data of consumers’ preferences and their social interactions. To characterize marketing resource allocation in networks, this paper develops a game theoretical model that allows for each firm’s own utility, action strategies of other firms and the inner state (self-belief and opinions) of consumers. In this model, firms can sway consumers’ opinions by spending marketing resources among consumers under budget and cost constraints. Each firm competes for the collective preference of consumers in a social network to maximize its utility. We derived the equilibrium strategies theoretically in a connected network and a dispersed network from the constructed model. These reveal that firms should allocate more marketing resources to some of consumers depending on their initial opinions, self-belief and positions in a network. We found that some structures of consumer networks may have an innate dominance for one firm, which can be retained in equilibrium results. This means that network structure can be as a tool for firms to improve their utilities. Furthermore, the sensitivities of budget and cost to the equilibria were analyzed. These results can provide some reference for resource allocation strategies in marketing competition.

Building a lightweight digital twin of a crane boom for structural safety monitoring based on a multi-fidelity surrogate model

Abstract: Undetected fatigue and overload damages at the key locations of the crane boom are among the biggest threats in construction, leading to structural failure. Thus, the structural health of the crane boom should be monitored in real-time to ensure that it works under the designed load capacity. In this work, we developed a lightweight digital twin by the multi-fidelity surrogate (MFS) model to improve the real-time monitoring and prediction accuracy of the structural safety of a crane boom. Digital twin technology, which can establish real-time mapping between the physical space and the digital space, has a promising potential for online monitoring and analysis of structures, equipment, and even human bodies. By combining the MFS model and sensor data, the lightweight digital twin can dynamically mirror the crane boom postures and predict its structural performance in real-time. In this study, the structural analysis of the crane boom is limited to the linear elastic stage of materials. Numerical experiments showed that the accuracy of the lightweight digital twin was enhanced compared with that established by the single-fidelity surrogate model, and the computational cost of the lightweight digital twin was decreased with respect to the digital twin built by the numerical method. Meanwhile, the uncertainty from the physical space was analyzed to enhance the reliability of the lightweight digital twin. Thus, the lightweight digital twin developed in our work can ensure accurate safety prediction and design optimization for crane booms.