Search

Aerogels, with their exceptional properties such as high insulation performance, lightness, and eco-friendliness, present immense potential for use in the construction industry. This paper reviews the progress in research and application of various types of aerogels in construction, highlighting notable case studies demonstrating energy efficiency gains and sustainability impacts. It delves into the lifecycle of aerogels, discussing environmental implications at each stage. Despite their potential, aerogels face certain limitations including production complexity, brittleness, and high cost which hinder their widespread adoption. The paper further discusses these challenges and highlights the need for continuous research and development efforts to overcome them, with a view to achieving more scalable production, enhanced mechanical properties, and cost reduction. Looking forward, the paper discusses the expansive horizon of aerogel research, emphasizing the potential of smart aerogels, their integration with renewable energy systems, and exploring the possibility of recycling or creating bio-based aerogels to enhance their overall sustainability. While still in early stages, aerogels promise to be a major contributor to the future of construction, leading to more sustainable and efficient built environments. [ABSTRACT FROM AUTHOR]

Since 2012, researchers have explored various factors influencing Bitcoin prices. Up until the end of July 2023, more than 9100 research papers on cryptocurrencies were published and indexed in the Web of Science Clarivate platform. The objective of this paper is to analyze the impact of publications on Bitcoin prices. This study aims to uncover significant themes within these research articles, focusing on cryptocurrencies in general and Bitcoin specifically. The research employs latent Dirichlet allocation to identify key topics from the unstructured abstracts. To determine the optimal number of topics, perplexity and topic coherence metrics are calculated. Additionally, the abstracts are processed using BERT-transformers and Word2Vec and their potential to predict Bitcoin prices is assessed. Based on the results, while the research helps in understanding cryptocurrencies, the potential of academic publications to influence Bitcoin prices is not significant, demonstrating a weak connection. In other words, the movements of Bitcoin prices are not influenced by the scientific writing in this specific field. The primary topics emerging from the analysis are the blockchain, market dynamics, transactions, pricing trends, network security, and the mining process. These findings suggest that future research should pay closer attention to issues like the energy demands and environmental impacts of mining, anti-money laundering measures, and behavioral aspects related to cryptocurrencies. [ABSTRACT FROM AUTHOR]

The hydraulic turbine has been used extensively in the field of energy conservation. For turbines that have low heads and large discharge, improving recovery efficiency and stability is crucial due to their significant hydraulic impact. This paper provides a detailed analysis of the correlation between the influence of radial guide vanes on the stability of low-head, large-discharge turbines focusing on hydraulic performance and energy dissipation before and after the implementation of guide vanes. Moreover, in this paper, two types of turbines, with and without guide vanes, were designed considering the desulfurization scenario. Hydraulic efficiency, radial force, and internal flow field mechanics were numerically studied, and validated through experiments. The results reveal that the working range of the hydraulic turbine could be widened and the energy recovery efficiency improved by a maximum of 3.11% in the small flow rate under the action of guide vanes. Furthermore, it results in a substantial reduction in the radial force of the impeller. Subsequently, the variation in entropy production of different components under full flow rate conditions was compared between the models with and without guide vanes. The total energy consumption decreases sharply under overall working conditions due to the flow control ability of guide vanes affecting the flow state. The entropy production rate of the impeller remains the largest regardless of the presence of a guide vane in the turbine. The vortices inside the guide vanes increase obviously with the flow rate increase. [ABSTRACT FROM AUTHOR]

In this paper, we present a new fuzzy symbolization technique for energy load forecasting with neural networks, FPLS-Sym. Symbolization techniques transform a numerical time series into a smaller string of symbols, providing a high-level representation of time series by combining segmentation, aggregation and discretization. The dimensional reduction obtained with symbolization can speed up substantially the time required to train neural networks, however, it can also lead to considerable information losses that could lead to a less accurate forecast. FPLS-Sym introduces the use of fuzzy logic in the discretization process, maintaining more information about each segment of the neural network at the expense of requiring more space in memory. Extensive experimentation was made to evaluate FPLS-Sym with various neural-network-based models, including different neural network architectures and activation functions. The evaluation was done with energy demand data from Spain taken from 2009 to 2019. Results show that FPLS-Sym provides better quality metrics than other symbolization techniques and outperforms the use of the standard numerical time series representation in both quality metrics and training time. [ABSTRACT FROM AUTHOR]

Electricity is establishing ground as a means of energy, and its proportion will continue to rise in the next generations. Home energy usage is expected to increase by more than 40% in the next 20 years. Therefore, to compensate for demand requirements, proper planning and strategies are needed to improve home energy management systems (HEMs). One of the crucial aspects of HEMS are proper load forecasting and scheduling of energy utilization. Energy management systems depend heavily on precise forecasting and scheduling. Considering this scenario, this article was divided into two parts. Firstly, this article gives a thorough analysis of forecasting models in HEMs with the primary goal of determining whichever model is most appropriate in a given situation. Moreover, for optimal utilization of scheduling strategies in HEMs, the current literature has discussed a number of scheduling optimization approaches. Therefore, secondly in this article, these approaches will be examined thoroughly to develop effective operating scheduling and to make wise judgments regarding usage of these techniques in HEMs. Finally, this paper also presents the future technical advancements and research gaps in load forecasting and scheduling and how they affect HEMs activities in the near future. [ABSTRACT FROM AUTHOR]

To address the limitations of 5 G, 6 G wireless networks are envisaged to provide sub-millisecond latency, ultra-high connection density, extremely high data rates, better coverage, reliability, availability, etc., for cloud computing, Internet-of-Everything, and cyber-physical systems. However, avoiding several trust-related issues in the design of the wireless networks may delay the goal. In recent years, Blockchain has become a promising technology that can transform various fields by providing innovative and profitable solutions. Blockchain's intrinsic features, such as immutability, decentralization, anonymity, and transparency, can corroborate trust among isolated networks to secure access control, authentication, better integrity, secrecy, and effectual resource sharing. This paper paints a holistic picture of 6 G wireless networks and Blockchain, including architecture, Blockchain-assisted 6 G services, deployment, 6 G-assisted Blockchain service models, consensus mechanisms for Blockchain-as-a-Service, 6 G-assisted mining and consensus, and others. It also sheds light on the integration possibilities of Blockchain and 6 G, its potential benefits, and the effects of consensus mechanisms on 6 G concerning security, scalability, and energy consumption, to name a few. The existing 6 G projects and applications have also been summarized, along with the discussion on research and projects to standardize 6 G. Various research directions for Blockchain, 6 G, and their integration have also been explored and reported. [ABSTRACT FROM AUTHOR]

In this research paper, we propose a novel hybrid deep learning approach, SSA-CNN-LSTM, for forecasting solar power generation. The approach combines Singular Spectrum Analysis (SSA), Convolutional Neural Networks (CNNs), and Long Short-Term Memory (LSTM) networks to leverage temporal and spatial dependencies in real-time greenhouse solar power generation data. Through a comprehensive comparative analysis, SSA-CNN-LSTM is compared against three established models, CNN-LSTM, SSA-CNN, and SSA-LSTM, employing real solar power generation data over a two-year period. The findings prominently demonstrate SSA-CNN-LSTM's exceptional performance, particularly in the 1-hour ahead prediction horizon. With an hour-ahead Mean Absolute Error (MAE) of 0.1202, SSA-CNN-LSTM surpasses the forecast precision of CNN-LSTM (0.6269), SSA-CNN (0.2354), and SSA-LSTM (0.2049). This excellence extends to the 2-hour-ahead forecast, where SSA-CNN-LSTM maintains its superiority with an MAE of 0.1400. In the day-ahead forecast, SSA-CNN-LSTM upholds its competitiveness, demonstrating an MAE of 0.1774. These outcomes underscore the immense potential of SSA-CNN-LSTM as a formidable tool for precise solar power forecasting. The model's effectiveness empowers greenhouse operators and energy management systems to optimize resource allocation, ultimately fostering elevated energy efficiency and overall greenhouse productivity. [ABSTRACT FROM AUTHOR]

Blockchain is a decentralized digital ledger technology that offers transparency, security, and immutability by recording and verifying transactions across multiple computers. However, it faces limitations such as scalability issues, high energy consumption, slow transaction validation, expanding storage requirements. Understanding these limitations is crucial for evaluating blockchain's suitability for specific use cases and finding solutions to overcome these challenges. Recognizing these limitations is imperative for assessing blockchain's applicability and devising solutions. This paper introduces a hybrid mechanism, ShardedScale, designed to enhance scalability, throughput, and system performance while mitigating time, gas, and ether consumption. The proposed ShardedScale approach combines various techniques including Proof of Work (PoW), Dynamic On-demand Proof of Stake (DPOS), Inter Planetary File System (IPFS), and sharding. ShardedScale achieved a remarkable 40% reduction in transaction confirmation times, a substantial 60% improvement in throughput, and demonstrated cost-effectiveness with a 30% reduction in gas price consumption and a 25% decrease in ethers consumption. These findings signify a significant step towards addressing challenges faced by traditional blockchain systems, providing valuable insights for future designs in blockchain technology. [ABSTRACT FROM AUTHOR]

Because of the phenomenal expansion of Internet of Things (IoT) devices around the world, Wireless Sensor Networks (WSN) have become increasingly important among the technical community, and research in this area has been growing exponentially. Researchers have used a variety of WSN technologies to address issues such as processing power constraints, bandwidth-limited connections, delays and energy consumption outlines that arise with sensor networks. However, in terms of delay optimization, affordability and effective energy consumption, WSN is the most suitable and alluring technology. This paper uses an Enhanced Scheduling Algorithm (ESA) with a probabilistic approach called Random Classical Game Theory (RCGT) to reduce the delay in WSN. Retransmissions are minimized when ESA and RCGT are used, which improve WSN delay. The idea is to improve the scheduling algorithm by using RCGT to lengthen the lifespan of the entire network. It has been demonstrated that the improved technique outperforms the existing algorithms in terms of throughput, energy consumption, hop count, delay and lifespan ratio. [ABSTRACT FROM AUTHOR]