Wind energy, as one of the renewable energies with the most potential for development, has been widely concerned by many countries. However, due to the great volatility and uncertainty of natural ...wind, wind power also fluctuates, seriously affecting the reliability of wind power system and bringing challenges to large-scale grid connection of wind power. Wind speed prediction is very important to ensure the safety and stability of wind power generation system. In this paper, a new wind speed prediction scheme is proposed. First, improved hybrid mode decomposition is used to decompose the wind speed data into the trend part and the fluctuation part, and the noise is decomposed twice. Then wavelet analysis is used to decompose the trend part and the fluctuation part for the third time. The decomposed data are classified. The long- and short-term memory neural network optimized by the improved particle swarm optimization algorithm is used to train the nonlinear sequence and noise sequence, and the autoregressive moving average model is used to train the linear sequence. Finally, the final prediction results were reconstructed. This paper uses this system to predict the wind speed data of China’s Changma wind farm and Spain’s Sotavento wind farm. By experimenting with the real data from two different wind farms and comparing with other predictive models, we found that (1) by improving the mode number selection in the variational mode decomposition, the characteristics of wind speed data can be better extracted. (2) According to the different characteristics of component data, the combination method is selected to predict modal components, which makes full use of the advantages of different algorithms and has good prediction effect. (3) The optimization algorithm is used to optimize the neural network, which solves the problem of parameter setting when establishing the prediction model. (4) The combination forecasting model proposed in this paper has clear structure and accurate prediction results. The research work in this paper will help to promote the development of wind energy prediction field, help wind farms formulate wind power regulation strategies, and further promote the construction of green energy structure.
Industry 4.0 aims to create a modern industrial system by introducing technologies, such as cloud computing, intelligent robotics, and wireless sensor networks. In this article, we consider the ...multichannel access and task offloading problem in mobile-edge computing (MEC)-enabled industry 4.0 and describe this problem in multiagent environment. To solve this problem, we propose a novel multiagent deep reinforcement learning (MADRL) scheme. The solution enables edge devices (EDs) to cooperate with each other, which can significantly reduce the computation delay and improve the channel access success rate. Extensive simulation results with different system parameters reveal that the proposed scheme could reduce computation delay by 33.38% and increase the channel access success rate by 14.88% and channel utilization by 3.24% compared to the traditional single-agent reinforcement learning method.
Android has a large number of users that are accumulating with each passing day. Security of the Android ecosystem is a major concern for these users with the provision of quality services. In this ...paper, multimodal analysis of malware apps has been presented. We exploit static, dynamic, and visual features of apps to predict the malicious apps using information fusion. The proposed study applies case-based reasoning; for catalyzing the process of training and validation over renowned datasets with enriched feature-set. Our proposed semi-supervised technique uses benign and malicious apps to predict and classify malware. The prediction process uses a hybrid analysis of malware. The proposed approach, due to the efficient and adaptive nature of CBR, outperforms prevalent approaches. Our approach has an accuracy of 95% and reduced rate of false negative rate and a better precision metric, which beat the state-of-the-art techniques.
Thermal-aware
(TA) task allocation is one of the most effective software-based dynamic thermal management techniques to minimize energy consumption in
data centers
(DCs). Compared to its ...counterparts, TA scheduling attains significant gains in energy consumption. However, the existing literature overlooks the heterogeneity of computing elements in terms of thermal constraints while allocating or migrating user jobs, which may significantly affect the reliability of racks and all the equipment therein. Moreover, the workload distribution among these racks/servers is not fair and efficient in terms of thermal footprints; it is potentially beneficial to determine the workload proportion for each computing node (rack/server) based on its marginal contribution in disturbing the thermal uniformity (TU) in a DC environment. To solve the said problems, we model the workload distribution in DCs as a coalition formation game with the
Shapley Value
(SV) solution concept. Also, we devise Shapley Workload (SW), a TA scheduling scheme based on the SV to optimize the TU and minimize the cooling cost of DCs. Specifically, the scheduling decisions are based on the ambient effect of the neighboring nodes, for the ambient temperature is affected by the following two factors: (1) the current temperature of computing components and (2) the physical organization of computing elements. This results in lower temperature values and better TU, consequently leading to lower cooling costs. Simulation results demonstrate that the proposed strategy greatly reduces the total energy consumption compared to the existing state-of-the-art.
Nano‐fluids' application for enhanced oil recovery (EOR) has attracted noticeable attention and formed a new research area in recent years. Currently, the greatest challenge in this area is to ...formulate stable nano‐fluids for oil reservoirs with high temperatures and salinity. To overcome the limitations of its application in high‐temperature drilling, polymer‐coated nanoparticles (SiO2‐PAMPS NPs) were prepared via solution polymerization of 2‐acrylamide‐2‐methyl‐1‐propane sulphonic acid (AMPS) from the surface of aminopropyl‐functionalized silica nanoparticles. The SiO2‐PAMPS NPs were characterized by Fourier‐transform infrared spectroscopy (FTIR), thermogravimetric analysis (TGA), scanning electron microscopy (SEM), and dynamic light scattering (DLS). The results indicated that the AMPS was successfully grafted onto the surface of silica nanoparticles, and the average diameter of SiO2‐PAMPS NPs was about 16 nm. The nano‐fluids showed noticeable stability in American Petroleum Institute (API) brine (2 wt.% CaCl2 and 8 wt.% NaCl) at 90°C beyond 46 days. When amphipathic nanoparticles were introduced to brine at 90°C, the potential of the nano‐fluids in recovering oil was evaluated by investigating the interfacial tension with kerosene oil and the oil contact angle in the nano‐fluids. The contact angle of the glass sheet surface before treatment was about 144°, while after SiO2‐PAMPS NPs treatment for 72 h, it became about 92°. Meanwhile, the nano‐fluids showed an excellent enhancing emulsibility property, which plays a vital role in promoting the development of EOR in high‐temperature and high‐salt environments.
The aim of this study is to explore the effectiveness and indications of using an anatomical implant through an inframammary fold incision for the treatment of breast ptosis. A retrospective analysis ...was conducted on cases of mild breast ptosis treated with this technique from November 2019 to February 2023. The preoperative and postoperative ptosis distances were recorded and subjected to statistical analysis for significant differences (
P
< 0.05). A total of 25 cases, comprising 50 breasts, were included in the study (
n
= 50). The preoperative ptosis distance was 1.29 ± 0.056 cm, and the postoperative ptosis distance was 0.072 ± 0.024 cm, showing a statistically significant difference (
P
< 0.05). All patients expressed satisfaction with the esthetic outcomes, and no complications such as double bubble deformity were observed. The use of anatomical implants through an inframammary fold incision for the treatment of mild breast ptosis demonstrates effective and safe clinical outcomes.
High-entropy alloys (HEAs) open up new doors for their novel design principles and excellent properties. In order to explore the huge compositional and microstructural spaces more effectively, ...high-throughput calculation techniques are put forward, overcoming the time-consuming and laboriousness of traditional experiments. Here we present and discuss four different calculation methods that are usually applied to accelerate the development of novel HEA compositions, that is, empirical models, first-principles calculations, calculation of phase diagrams (CALPHAD), and machine learning. The empirical model and the machine learning are both based on summary and analysis, while the latter is more believable for the use of multiple algorithms. The first-principles calculations are based on quantum mechanics and several open source databases, and it can also provide the finer atomic information for the thermodynamic analysis of CALPHAD and machine learning. We illustrate the advantages, disadvantages, and application range of these techniques, and compare them with each other to provide some guidance for HEA study.
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