Search

Satellite-Drone Image Cross-View Geolocalization has wide applications. Due to the pronounced variations in the visual features of 3D objects under different angles, Satellite-Drone cross-view image geolocalization remains an unresolved challenge. The key to successful cross-view geolocalization lies in extracting crucial spatial structure information across different scales in the image. Recent studies improve image matching accuracy by introducing an attention mechanism to establish global associations among local features. However, existing methods primarily focus on using single-scale features and employ a single-channel attention mechanism to correlate local convolutional features from different locations. This approach inadequately explores and utilizes multi-scale spatial structure information within the image, particularly lacking in the extraction and utilization of locally valuable information. In this paper, we propose a cross-view image geolocalization method based on multi-scale information and a dual-channel attention mechanism. The multi-scale information includes features extracted from different scales using various convolutional slices, and it extensively utilizes shallow network features. The dual-channel attention mechanism, through successive local and global feature associations, effectively learns depth discriminative features across different scales. Experimental results were conducted using existing satellite and drone image datasets, with additional validation performed on an independent self-made dataset. The findings indicate that our approach exhibits superior performance compared to existing methods. The methodology presented in this paper exhibits enhanced capabilities, especially in the exploitation of multi-scale spatial structure information and the extraction of locally valuable information. [ABSTRACT FROM AUTHOR]

High-rise building areas (HRBs) provide significant social and environmental services and play a crucial role in modern urbanization. The large-scale and long-term spatial distribution of HRBs is of great interest to many fields, such as urban planning and local climate analysis. While previous studies have confirmed the value of Sentinel-2 images in extracting HRBs and their changes, current work is limited to relatively local areas and short-term analysis. One reason is due to the fact that the earliest Sentinel-2 image can only date back to 2015. To address this research gap, this paper proposes an efficient procedure to intelligently extract HRBs and their changes from multitemporal Landsat-7 and Sentinel-2 images, using a specifically designed fully convolutional network. To validate the proposed method, we selected four typical cities in China, namely, Beijing, Shanghai, Guangzhou, and Zhengzhou, as study areas. We utilized Landsat-7 images acquired in 2000 and 2010, along with Sentinel-2 images acquired in 2020, as experimental data. We extracted and analyzed three periods of HRBs and their changes in the four cities, along with urban rail terminal data and gross domestic product (GDP) data in the same period. The results show that the proposed method can efficiently extract HRBs and their changes in the four cities over the past 20 years, with an overall accuracy of more than 90%. HRBs changes are primarily driven by urban planning policies and geographical factors. There is a strong positive correlation between the increase in HRBs and the increase in rail terminals, both in terms of quantity and spatial distribution. Additionally, there is a positive correlation between HRBs increase and GDP increase in terms of quantity, but the trend varies in different cities due to their diverse developing modes. Overall, the results indicate that the proposed method can be a potential operational tool to extract large-scale and long-term HRBs and their changes in China. [ABSTRACT FROM AUTHOR]

Since 2018, the outbreak and prevalence of the African swine fever virus (ASFV) in China have caused huge economic losses. Less virulent ASFVs emerged in 2020, which led to difficulties and challenges for early diagnosis and control of African swine fever (ASF) in China. An effective method of monitoring ASFV antibodies and specific antibodies against ASFV to promote the development of prevention techniques are urgently needed. In the present study, ASFV p17 was successfully expressed in CHO cells using a suspension culture system. An indirect enzyme-linked immunosorbent assay (ELISA) based on purified p17 was established and optimized. The monoclonal antibody (mAb) against p17 recognized a conservative linear epitope (3TETSPLLSH11) and exhibited specific reactivity, which was conducive to the identification of recombinant porcine reproductive and respiratory syndrome virus (PRRSV) expressing p17. The ELISA method efficiently detected clinical ASFV infection and effectively monitored the antibody levels in vivo after recombinant PRRSV live vector virus expressing p17 vaccination. Overall, the determination of the conserved linear epitope of p17 would contribute to the in-depth exploration of the biological function of the ASFV antigen protein. The indirect ELISA method and mAb against ASFV p17 revealed efficient detection and promising application prospects, making them ideal for epidemiological surveillance and vaccine research on ASF. [ABSTRACT FROM AUTHOR]

Because of their small size and large specific surface area, nanoparticles (NPs) have special properties that are different from bulk materials. In particular, Au/Ag NPs have been intensively studied for a long time, especially for biomedical applications. Thereafter, they played a significant role in the fields of biology, medical testing, optical imaging, energy and catalysis, MRI contrast agents, tumor diagnosis and treatment, environmental protection, and so on. When synthesizing Au/Ag NPs, the laser ablation and biosynthesis methods are very promising green processes. Therefore, this review focuses on the progress in the laser ablation and biological synthesis processes for Au/Ag NP generation, especially in their fabrication fundamentals and potential applications. First, the fundamentals of the laser ablation method are critically reviewed, including the laser ablation mechanism for Au/Ag NPs and the controlling of their size and shape during fabrication using laser ablation. Second, the fundamentals of the biological method are comprehensively discussed, involving the synthesis principle and the process of controlling the size and shape and preparing Au/Ag NPs using biological methods. Third, the applications in biology, tumor diagnosis and treatment, and other fields are reviewed to demonstrate the potential value of Au/Ag NPs. Finally, a discussion surrounding three aspects (similarity, individuality, and complementarity) of the two green synthesis processes is presented, and the necessary outlook, including the current limitations and challenges, is suggested, which provides a reference for the low-cost and sustainable production of Au/Ag NPs in the future. [ABSTRACT FROM AUTHOR]

In this era of information explosion, smartphones have become a necessary device in our daily life. In order to select a better smartphone, most users try to collect more attributes to help them purchase their own smartphones, including the brand image from the advertisements, features from the specifications, word-of-mouth from their peers, and the average sales from some secondary data webs. In order to assist the users to evaluate the brand performance from the market attributes, in this paper, we selected nine smartphone brands and used multi-criteria decision-making methods to rank the smartphones' functions. We first use TOPSIS to evaluate word-of-mouth, together with average sales collected from the website of each brand, and the brand image obtained by the use of questionnaires. Finally, we summarize the final rankings of these smartphone brands. The brand performance analysis shows that our proposed hybrid method can significantly derive the overall rankings of smartphone brands. [ABSTRACT FROM AUTHOR]

High-rise buildings (HRBs) as a modern and visually distinctive land use play an important role in urbanization. Large-scale monitoring of HRBs is valuable in urban planning and environmental protection and so on. Due to the complex 3D structure and seasonal dynamic image features of HRBs, it is still challenging to monitor large-scale HRBs in a routine way. This paper extends our previous work on the use of the Fully Convolutional Networks (FCN) model to extract HRBs from Sentinel-2 data by studying the influence of seasonal and spatial factors on the performance of the FCN model. 16 Sentinel-2 subset images covering four diverse regions in four seasons were selected for training and validation. Our results indicate the performance of the FCN-based method at the extraction of HRBs from Sentinel-2 data fluctuates among seasons and regions. The seasonal change of accuracy is larger than that of the regional change. If an optimal season can be chosen to get a yearly best result, F1 score of detected HRBs can reach above 0.75 for all regions with most errors located on the boundary of HRBs. FCN model can be trained on seasonally and regionally combined samples to achieve similar or even better overall accuracy than that of the model trained on an optimal combination of season and region. Uncertainties exist on the boundary of detected results and may be relieved by revising the definition of HRBs in a more rigorous way. On the whole, the FCN based method can be largely effective at the extraction of HRBs from Sentinel-2 data in regions with a large diversity in culture, latitude, and landscape. Our results support the possibility to build a powerful FCN model on a larger size of training samples for operational monitoring HRBs at the regional level or even on a country scale. [ABSTRACT FROM AUTHOR]

Highlights: EIS was used to estimate the SOH of LIBs found to be fast and effective. It is more convenient to use CNN to extract features of EIS data automatically. The improved ECM method and IPSO-CNN-BiLSTM method are proposed in this paper. IPSO algorithm was first proposed to applied to the problem of optimizing the initial parameters of the neural networks. The state of health (SOH) is critical to the efficient and reliable use of lithium-ion batteries (LIBs). Recently, the SOH estimation method based on electrochemical impedance spectroscopy (EIS) has been proven effective. In response to different practical applications, two models for SOH estimation are proposed in this paper. Aiming at based on the equivalent circuit model (ECM) method, a variety of ECMs are proposed. Used EIS to predict the ECM, the results show that the improved method ensures the correctness of the ECM and improves the estimation results of SOH. Aiming at a data-driven algorithm, proposes a convolution neural network (CNN) to process EIS data which can not only extract the key points but also simplifies the complexity of manual feature extraction. The bidirectional long short-term memory (BiLSTM) model was used for serial regression prediction. Moreover, the improved Particle Swarm Optimization (IPSO) algorithm is proposed to optimize the model. Comparing the improved model (IPSO-CNN-BiLSTM) with the traditional PSO-CNN-BiLSTM, CNN-BiLSTM and LSTM models, the prediction results are improved by 13.6%, 93.75% and 94.8%, respectively. Besides that, the two proposed methods are 27% and 35% better than the existing gaussion process regression (GPR) model, which indicates that the proposed improved methods are more flexible for SOH estimation with higher precision. [ABSTRACT FROM AUTHOR]

Ozone (O3) is an important secondary substance that plays a significant role in atmospheric chemistry and climate change. Although O3 is essential in the stratosphere, it is harmful to human health in the troposphere, where this study was conducted. In recent years, O3 pollution in the Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD), and Pearl River Delta (PRD) regions has deteriorated, which has become an important environmental problem. The generation of O3 is closely related to meteorological factors. In this study, the weather classification method was adopted to study the effect of meteorological conditions on O3 concentration. In the BTH region, Tianjin was selected as the representative city for the research. The real-time pollutants data, meteorological re-analysis data, and meteorological data in 2019 were combined for the analysis. The subjective weather classification method was adopted to investigate the effects of different weather types on O3 concentration. The backward trajectory tracking model was used to explore the characteristics and changes of O3 pollution under two extreme weather types. The results indicate there is a good correlation between O3 concentration and ambient temperature. Under the control of low pressure on the ground and the influence of southwest airflow in the upper air for Tianjin, heavy O3 pollution occurred frequently. The addition of external transport and local generation will cause high O3 values when the weather system is weak. The O3 concentration is closely related to ambient temperature. Continuous high-temperature weather is conducive to the photochemical reaction. The multi-day O3 pollution process would occur when the weather system is robust. The first and second types of extreme weather are more likely to cause persistent O3 pollution processes. Under the premise of stable emission sources, the change in weather patterns was the main reason affecting the O3 concentration. This study aims to improve O3 pollution control and air quality prediction in the BTH region and large cities in China. [ABSTRACT FROM AUTHOR]

Cladophora qinghaiensis, an endemic species of Cladophora in saltwater lakes, was scientifically named in 2021 (hereafter referred to as Cladophora). Cladophora exists in different morphologies, including attached submerged Cladophora (AC), grown floating Cladophora (GFC), and death floating Cladophora (DFC). Previous satellite remote sensing has mainly focused on identifying floating algae. In this study, Qinghai Lake served as a case study, and a classification decision tree model (CDTM) was proposed. The model employed the chlorophyll spectral index (CSI) and the normalized difference vegetation index (NDVI) to differentiate AC, Floating Cladophora (FC), and water. Additionally, the floating Cladophora index (FCI) was introduced to further distinguish GFC and DFC within FC. The method was applicable to Sentinel-2 images from 2016–2023. Visual interpretation methods were used for Landsat series images from the summer months (July to September) to obtain the AC and FC. The results demonstrate that over the past 30 years, the areas inhabited by AC and FC have increased gradually. The three morphologies of Cladophora also exhibited seasonal variations, with growth observed annually in May–June, reaching peaks in August–September, and gradually declining in October. In addition, by combining factors such as water surface area and climatic factors, we analyzed the driving forces influencing the changes in Cladophora. In this research, AC and FC showed significant correlations with the water surface area, with correlation coefficients (r) of approximately 0.9 and 0.7, respectively. These new findings provide valuable insights regarding the spatiotemporal changes and underlying causes for different morphologies of Cladophora in global saline lakes. [ABSTRACT FROM AUTHOR]

This paper studies the use of the Fully Convolutional Networks (FCN) model in the extraction of water bodies from Very High spatial Resolution (VHR) optical images in the case of limited training samples. Two different seasonal GaoFen-2 images with a spatial resolution of 0.8 m in the south of the Beijing metropolitan area were used to extensively validate the FCN model. Four key factors including input features, training data, transfer learning, and data augmentation related to the performance of the FCN model were empirically analyzed by using 36 combinations of various parameter settings. Our findings indicate that the FCN-based method can work as a robust and cost-effective tool in the extraction of water bodies from VHR images. The FCN-based method trained on a small amount of labeled L1A data can also significantly outperform the Normalized Difference Water Index (NDWI) based method, the Support Vector Machine (SVM) based method, and the Sparsity Model (SM) based method, even when radiometric normalization and spatial contexts are introduced to preprocess the input data for the latter three methods. The advantages of the FCN-based method are mainly due to its capability to exploit spatial contexts in the image, especially in urban areas with mixed water and shadows. Though the settings of four key factors significantly affect the performance of the FCN based method, choosing a qualified setting for the FCN model is not difficult. Our lessons learned from the successful use of the FCN model for the extraction of water from VHR images can be extended to extract other land covers. [ABSTRACT FROM AUTHOR]

As an important energy storage device, lithium-ion batteries (LIBs) have been widely used in various fields due to their remarkable advantages. The high level of precision in estimating the battery's state of health greatly enhances the safety and dependability of the application process. In contrast to traditional model-based prediction methods that are complex and have limited accuracy, data-driven prediction methods, which are considered mainstream, rely on direct data analysis and offer higher accuracy. Therefore, this paper reviews how to use the latest data-driven algorithms to predict the SOH of LIBs, and proposes a general prediction process, including the acquisition of datasets for the charging and discharging process of LIBs, the processing of data and features, and the selection of algorithms. The advantages and limitations of various processing methods and cutting-edge data-driven algorithms are summarized and compared, and methods with potential applications are proposed. Effort was also made to point out their application methods and application scenarios, providing guidance for researchers in this area. [ABSTRACT FROM AUTHOR]

Highlights: What are the main findings? Rapid acquisition technology of electrochemical impedance spectroscopy. EIS was used to quickly and effectively estimate the SOH of LIBs. Machine learning method and equivalent circuit method to estimate battery health state. What is the implication of the main finding? EIS were found to be better SOH estimation method. The technology based on EIS has the characteristics of high precision advantages. The method based on EIS of LIBs SOH estimation has been applied to the BMS. Lithium-ion batteries stand out from other clean energy sources because of their high energy density and small size. With the increasing application scope and scale of lithium-ion batteries, real-time and accurate monitoring of its state of health plays an important role in ensuring the healthy and stable operation of an energy storage system. Due to the interaction of various aging reactions in the aging process of lithium-ion batteries, the capacity attenuation shows no regularity. However, the traditional monitoring method is mainly based on voltage and current, which cannot reflect the internal mechanism, so the accuracy is greatly reduced. Recently, with the development of electrochemical impedance spectroscopy, it has been possible to estimate the state of health quickly and accurately online. Electrochemical impedance spectroscopy can measure battery impedance in a wide frequency range, so it can reflect the internal aging state of lithium-ion batteries. In this paper, the latest impedance spectroscopy measurement technology and electrochemical impedance spectroscopy based on lithium-ion battery health state estimation technology are summarized, along with the advantages and disadvantages of the summary and prospects. This fills the gap in this aspect and is conducive to the further development of this technology. [ABSTRACT FROM AUTHOR]

Porcine reproductive and respiratory syndrome (PRRS) is an important viral disease, causing significant economic losses to the swine industry worldwide. Atypical cases caused by highly pathogenic PRRS virus (HP-PRRSV) emerged in 2006 in China. The vaccine strain HuN4-F112 has been developed from the wild-type HP-PRRSV HuN4 through repeated passages on MARC-145 cells. However, the mechanisms of attenuation have yet to be defined. Previous studies have shown that the vaccine strain HuN4-F112 could not effectively replicate in porcine alveolar macrophages (PAMs). In the present study, a series of chimeric and mutant PRRSVs were constructed to investigate regions associated with the virus attenuation. Firstly, the corresponding genome regions (ORF1a, ORF1b and ORFs 2-7) were exchanged between two infectious clones of HuN4 and HuN4-F112, and then the influence of small regions in ORF1a and ORF2-7 was evaluated, then influence of specific amino acids on NSP2 was tested. NSP2 was determined to be the key gene that regulated infection efficiency on PAMs, and amino acids at 893 and 979 of NSP2 were the key amino acids. The results of in vivo study indicated that NSP2 was not only important for infection efficiency in vitro, but also influenced the virulence, which was indicated by the results of survival rate, temperature, viremia, lung score and tissue score. [ABSTRACT FROM AUTHOR]

Ship emissions are one of the main sources of air pollution in port cities. The prosperous maritime trade has brought great harm to the air quality of port cities while promoting the development of the world economy. During the berthing process, ship auxiliary machines emit a large amount of air pollutants, which have a great impact on air quality and public health. Alternative marine fuels are being studied and used frequently to reduce ship emissions. This research was carried out to investigate the gaseous and particles emission characteristics of a marine diesel engine during the application of experimental biodiesel fuels. To study the influence of mixed fuels on engine performance, measurements were made at different engine loads and speeds. Different diesel fuels were tested using various ratios between biodiesel and BD0 (ultra-low sulfur diesel) of 0%, 10%, 30%, 50%, 70%, 90%, and 100%. The results indicated the use of biodiesel has little influence on the combustion performance but has a certain impact on exhaust emissions. The octane number and laminar flame speed of biodiesel are higher than those of BD0, so the combustion time of the test diesel engine is shortened under the mixed mode of biodiesel. In addition, a high ratio of biodiesel leads to a decrease of the instantaneous peak heat release rate, causing the crank angle to advance. As the biodiesel blending ratio increased, most of the gaseous pollutants decreased, especially for CO, but it led to an increase of particle numbers. The particle size distribution exhibits a unimodal distribution under various conditions, with the peak value appearing at 30–75 nm. The use of biodiesel has no effect on this phenomenon. The peak positions strongly depend on fuel types and engine conditions. The particulate matter (PM) emitted from the test engine included large amounts of organic carbon (OC), which accounted for between 30% and 40% of PM. Whereas the elemental carbon (EC) accounted for between 10% and 20%, the water-soluble ions components accounted for 6–15%. Elemental components, which accounted for 3–8% of PM emissions, mainly consisted of Si, Fe, Sn, Ba, Al, Zn, V, and Ni. Generally, biodiesel could be a reliable alternative fuel to reduce ship auxiliary engine emissions at berth and improve port air quality. [ABSTRACT FROM AUTHOR]

Transparent nano-ceramics have an important high-transmittance, material-integrating structure and function and a variety of potential applications, such as use in infrared windows, optical isolators, composite armors, intelligent terminal screens, and key materials of solid-state lasers. Transparent ceramics were originally developed to replace single crystals because of their low fabricating cost, controllable shape, and variable composition. Therefore, this study reviews and summarizes the development trends in transparent nano-ceramics and their potential applications. First, we review the research progress and application of laser nano-ceramic materials, focusing on the influence of controllable doping of rare earth ions on thermal conductivity and the realization of large-scale fabrication technology. Second, the latest research progress on magneto-optical transparent nano-ceramics, mainly including terbium gallium garnet (Tb3Ga5O12, TGG) ceramics and terbium aluminum garnet (Tb3Al5O12, TAG) ceramics, are summarized, and their performance is compared. Third, the research progress of transparent armor nano-ceramic materials, represented by MgAl2O3 and Aluminum oxynitride (AlON), are reviewed. Lastly, the progress in electro-optical transparent nano-ceramics and scintillation transparent nano-ceramics is reported, and the influence of the material-fabrication process on electro-optic effect or luminous intensity is compared. Moreover, the effect of particle diameter on fabrication, the relationship between nano powder and performance, and different sintering methods are discussed. In summary, this study provides a meaningful reference for low-cost and sustainable production in the future. [ABSTRACT FROM AUTHOR]

Porcine reproductive and respiratory syndrome virus (PRRSV) is one of the most important pathogens in the swine industry worldwide. Our previous study had indicated that proprotein convertase subtilisin/kexin type 9 (PCSK9) was a responsive gene in porcine alveolar macrophages (PAMs) upon PRRSV infection. However, whether PCSK9 impacts the PRRSV replication and how the PRRSV modulates host PCSK9 remains elusive. Here, we demonstrated that PCSK9 protein suppressed the replication of both type-1 and type-2 PRRSV species. More specifically, the C-terminal domain of PCSK9 was responsible for the antiviral activity. Besides, we showed that PCSK9 inhibited PRRSV replication by targeting the virus receptor CD163 for degradation through the lysosome. In turn, PRRSV could down-regulate the expression of PCSK9 in both PAMs and MARC-145 cells. By screening the nonstructural proteins (nsps) of PRRSV, we showed that nsp11 could antagonize PCSK9's antiviral activity. Furthermore, mutagenic analyses of PRRSV nsp11 revealed that the endoribonuclease activity of nsp11 was critical for antagonizing the antiviral effect of PCSK9. Collectively, our data provide further insights into the interaction between PRRSV and the cell host and offer a new potential target for the antiviral therapy of PRRSV. [ABSTRACT FROM AUTHOR]

Ester-functionalized styrene-butadiene rubber (dibutyl itaconate-styrene-butadiene rubber) (D-ESBR) was synthesized by low-temperature emulsion polymerization using dibutyl itaconate (DBI) as a modified monomer containing ester groups. Nonpetroleum-based silica with hydroxy groups was used as a filler to enhance the D-ESBR, which can provide excellent mechanical properties, low rolling resistance, and high wet skid resistance. During the preparation of the silica/D-ESBR nanocomposites, a hydrogen-bonding interface was formed between the hydroxy groups on the surface of silica and the ester groups in the D-ESBR macromolecules. As the content of ester groups in the D-ESBR increases, the dispersion of silica in the nanocomposites is gradually improved, which was verified by rubber process analyzer (RPA) and scanning electron microscopy (SEM). Overall mechanical properties of the silica/D-ESBR modified with 5 wt % DBI were improved and became superior to that of the non-modified nanocomposite. Compared with the non-modified silica/D-ESBR, the DBI modified silica/D-ESBR exhibited a lower tan δ value at 60 °C and comparable tan δ value at 0 °C, indicating that the DBI modified silica/D-ESBR had lower rolling resistance without sacrificing wet skid resistance. [ABSTRACT FROM AUTHOR]

Highly pathogenic porcine reproductive and respiratory syndrome virus PRRSV (HP-PRRSV) was one of the most devastating diseases of the pig industry, among various strategies, vaccination was one of the most useful tools for PRRS control. Attenuated live vaccine was used worldwide, however, the genetic basis of HP-PRRSV virulence change during attenuation remain to be determined. Here, to identify virulence associated regions of HP-PRRSV during attenuation in vitro, six full-length infectious cDNA clones with interchanges of 5′UTR + ORF1a, ORF1b, and ORF2-7 + 3′UTR regions between HP-PRRSV strain HuN4-F5 and its attenuated vaccine strain HuN4-F112 were generated, and chimeric viruses were rescued. Piglets were inoculated with chimeric viruses and their parental viruses, and rectal temperature were recorded daily, and serum were collected for future experiments. Our results showed that ORF1a played an important role on virus replication, cytokine response and lung damage, the exchange of ORF1b and ORF2-7 in different backbone led to different exhibition on virus replication in vivo/vitro and cytokine response. Among 9 PRRSV attenuated series, consistent amino acid changes during PRRSV attenuation were found in NSP4, NSP9, GP2, E, GP3 and GP4. Our study provides a fundamental data for the investigation of PRRSV attenuation, the different results of the virulence change among different studies indicated that different mechanisms might be used during PRRSV virulence enhancement in vivo and attenuation in vitro. [ABSTRACT FROM AUTHOR]

In this paper, we propose an optimized power distribution method for hybrid electric energy storage systems for electric vehicles (EVs). The hybrid energy storage system (HESS) uses two isolated soft-switching symmetrical half-bridge bidirectional converters connected to the battery and supercapacitor (SC) as a composite structure of the protection structure. The bidirectional converter can precisely control the charge and discharge of the SC and battery. Spiral wound SCs with mesoporous carbon electrodes are used as the energy storage units of EVs. Under the 1050 operating conditions of the EV driving cycle, the SC acts as a "peak load transfer" with a charge and discharge current of 2isc~3ibat. An improved energy allocation strategy under state of charge (SOC) control is proposed, that enables SC to charge and discharge with a peak current of approximately 4ibat. Compared with the pure battery mode, the acceleration performance of the EV is improved by approximately 50%, and the energy loss is reduced by approximately 69%. This strategy accommodates different types of load curves, and helps improve the energy utilization rate and reduce the battery aging effect. [ABSTRACT FROM AUTHOR]