Global trends in COVID-19 Alzheimer's related research: a bibliometric analysis

BackgroundThe COVID-19 pandemic has significantly impacted public health, putting people with Alzheimer's disease at significant risk. This study used bibliometric analysis method to conduct in-depth research on the relationship between COVID-19 and Alzheimer's disease, as well as to predict its development trends.MethodsThe Web of Science Core Collection was searched for relevant literature on Alzheimer's and Coronavirus-19 during 2019–2023. We used a search query string in our advanced search. Using Microsoft Excel 2021 and VOSviewer software, a statistical analysis of primary high-yield authors, research institutions, countries, and journals was performed. Knowledge networks, collaboration maps, hotspots, and regional trends were analyzed using VOSviewer and CiteSpace.ResultsDuring 2020–2023, 866 academic studies were published in international journals. United States, Italy, and the United Kingdom rank top three in the survey; in terms of productivity, the top three schools were Harvard Medical School, the University of Padua, and the University of Oxford; Bonanni, Laura, from Gabriele d'Annunzio University (Italy), Tedeschi, Gioacchino from the University of Campania Luigi Vanvitelli (Italy), Vanacore, Nicola from Natl Ctr Dis Prevent and Health Promot (Italy), Reddy, P. Hemachandra from Texas Tech University (USA), and El Haj, Mohamad from University of Nantes (France) were the authors who published the most articles; The Journal of Alzheimer's Disease is the journals with the most published articles; “COVID-19,” “Alzheimer's disease,” “neurodegenerative diseases,” “cognitive impairment,” “neuroinflammation,” “quality of life,” and “neurological complications” have been the focus of attention in the last 3 years.ConclusionThe disease caused by the COVID-19 virus infection related to Alzheimer's disease has attracted significant attention worldwide. The major hot topics in 2020 were: “Alzheimer' disease,” COVID-19,” risk factors,” care,” and “Parkinson's disease.” During the 2 years 2021 and 2022, researchers were also interested in “neurodegenerative diseases,” “cognitive impairment,” and “quality of life,” which require further investigation

Short-Term Power Prediction of a Wind Farm Based on Empirical Mode Decomposition and Mayfly Algorithm–Back Propagation Neural Network

With the improvement of energy consumption structure, the installed capacity of wind power increases gradually. However, the inherent intermittency and instability of wind energy bring severe challenges to the dispatching operation. Wind power forecasting is one of the main solutions. In this work, a new combined wind power prediction model is proposed. First, a quartile method is used for data cleaning, namely, identifying and eliminating the abnormal data. Then, the wind power data sequence is decomposed by empirical mode decomposition to eliminate non-stationary characteristics. Finally, the wind generator data are trained by the MA-BP network to establish the wind power prediction model. Also, the simulation tests verify the prediction effect of the proposed method. Specifically speaking, the average MAPE is decreased to 12.4979% by the proposed method. Also, the average RMSE and MAE are 107.1728 and 71.604 kW, respectively

Study on Short-Term Electricity Load Forecasting Based on the Modified Simplex Approach Sparrow Search Algorithm Mixed with a Bidirectional Long- and Short-Term Memory Network

In order to balance power supply and demand, which is crucial for the safe and effective functioning of power systems, short-term power load forecasting is a crucial component of power system planning and operation. This paper aims to address the issue of low prediction accuracy resulting from power load volatility and nonlinearity. It suggests optimizing the number of hidden layer nodes, number of iterations, and learning rate of bi-directional long- and short-term memory networks using the improved sparrow search algorithm, and predicting the actual load data using the load prediction model. Using actual power load data from Wuxi, Jiangsu Province, China, as a dataset, the model makes predictions. The results indicate that the model is effective because the enhanced sparrow algorithm optimizes the bi-directional long- and short-term memory network model for predicting the power load data with a relative error of only 2%, which is higher than the prediction accuracy of the other models proposed in the paper

An Efficient Methodology for License Plate Localization and Recognition with Low Quality Images

Abstract It is challenging to find an effective license plate detection and recognition method due to the different conditions during the image acquisition phase. This paper aims to develop a new accurate and efficient method based on color difference and SVM recognition model that yields better performance for vehicle images under low quality. The proposed method is tested with 200 images which involve many difficult conditions, such as low resolution, night-lighting, dirt, complicated background, and distortion problems. The experimental results demonstrate very satisfactory performance for license plate detection in terms of speed and accuracy and are better than the existing methods like edge detection or HSV color conversion method. The overall probability of localization is close to 100%, with a false recognition rate of 2%.</jats:p

A Parallel Partial Enhancement Method for License Plate Localization in Low-Quality Images

Finding an effective license plate localization (LPL) method is challenging owing to different conditions during the image acquisition phase. Most existing methods do not consider various low-quality image conditions that exist in real-world situations. Low-quality image conditions mean that an image can have low resolution, plate imperfection effects, variable illumination environments or background objects similar to the license plate (LP). To improve the anti-interference ability and the speed performance of algorithm, this study aims to develop a parallel partial enhancement method based on color differences that demonstrates improved localization performance for blue–white LP images under low-quality conditions. A novel color difference model is exploited to enhance LP areas and filter non-LP areas. Blue–white color ratio and projection analysis are performed to select the exact LP area from the candidates. Moreover, this study develops a parallel version based on a multicore CPU for real-time processing for industrial applications. An image database including 395 low-quality car images captured from various scenes under different conditions is tested for the performance evaluation. The extensive experiments show the effectiveness and efficiency of the proposed approach. </jats:p

Effective denitrification process by a low voltage in a multi-cathode bio-electrode film reactor

An outstanding nitrate removal rate of 16.8 NO3−–N mg L−1 h−1 was achieved by applying voltage of 0.25 V in a multi-cathode bio-electrode film reactor.</p