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This study introduces RTEEMF (Real-Time Evaluation Electromagnetic Field)-PhoneAnts, a novel Deep Learning (DL) framework for the efficient evaluation of mobile phone antenna performance, addressing the time-consuming nature of traditional full-wave numerical simulations. The DL model, built on convolutional neural networks, uses the Near-field Electromagnetic Field (NEMF) distribution of a mobile phone antenna in free space to predict the Effective Isotropic Radiated Power (EIRP), Total Radiated Power (TRP), and Specific Absorption Rate (SAR) across various configurations. By converting antenna features and internal mobile phone components into near-field EMF distributions within a Huygens' box, the model simplifies its input. A dataset of 7000 mobile phone models was used for training and evaluation. The model's accuracy is validated using the Wilcoxon Signed Rank Test (WSR) for SAR and TRP, and the Feature Selection Validation Method (FSV) for EIRP. The proposed model achieves remarkable computational efficiency, approximately 2000-fold faster than full-wave simulations, and demonstrates generalization capabilities for different antenna types, various frequencies, and antenna positions. This makes it a valuable tool for practical research and development (R&D), offering a promising alternative to traditional electromagnetic field simulations. The study is publicly available on GitHub for further development and customization. Engineers can customize the model using their own datasets. [ABSTRACT FROM AUTHOR]

Symmetrical solid oxide cells (SSOCs) are very useful for energy generation and conversion. To fabricate the electrode of SSOC, it is very time‐consuming to use the conventional approach. In this work, we design and develop a novel method, extreme heat treatment (EHT), to rapidly fabricate electrodes for SSOC. We show that by using the EHT method, the electrode can be fabricated in seconds (the fastest method to date), benefiting from enhanced reaction kinetics. The EHT‐fabricated electrode presents a porous structure and good adhesion with the electrolyte. In contrast, tens of hours are needed to prepare the electrode by the conventional approach, and the prepared electrode exhibits a dense structure with a larger particle size due to the lengthy treatment. The EHT‐fabricated electrode shows desirable electrochemical performance. Moreover, we show that the electrocatalytic activity of the perovskite electrode can be tuned by the vigorous approach of fast exsolution, deriving from the increased active sites for enhancing the electrochemical reactions. At 900 °C, a promising peak power density of 966 mW cm−2 is reached. Our work exploits a new territory to fabricate and develop advanced electrodes for SSOCs in a rapid and high‐throughput manner. [ABSTRACT FROM AUTHOR]

Background: Numerous studies have investigated the potential effects of transcranial direct current stimulation (tDCS) on improving symptoms related to Alzheimer's disease (AD). However, these studies have produced inconsistent results, leading to a need for further investigation. Methods: A comprehensive search was conducted, including articles published from the initial availability date to 5 April 2024. The extracted study data were analyzed using STATA 12.0 software. The standard mean difference (SMD) and a 95% confidence interval (CI) were calculated to assess the effects of tDCS. Results: A total of 18 studies assessing the effects of tDCS on AD were included in the study. The study revealed that tDCS has an immediate positive impact on general cognitive, executive, language, and visuospatial function. However, the study did not observe any other significant effect of tDCS treatment on improvements in brain function, including long‐term effects on general cognitive, attention, language, and memory function, as well as immediate effects on attention and memory function. Conclusions: In conclusion, the study suggests that tDCS may be a promising intervention for improving the cognitive function of patients with AD. However, given the complex and multifactorial nature of AD, further well‐designed studies with larger sample sizes are necessary to clarify the effectiveness of tDCS and determine the optimal combination of tDCS parameters. [ABSTRACT FROM AUTHOR]

After comparing the economic advantages of different methods for energy storage system capacity configuration and hybrid energy storage system (HESS) over single energy storage system, a method based on improved moving average and ensemble empirical mode decomposition (EEMD) to smooth wind power fluctuations is proposed aiming at the optimal capacity configuration of HESS. Firstly, EEMD was used to decompose the HESS reference power which was derived by improved moving average filtering, and then several intrinsic mode functions (IMFs) were obtained. Each IMF instantaneous frequency-time profile was processed by Hilbert transform, the so- call gap frequency was identified, and then reconstruct the high and low frequency components to obtain the HESS reference power. Subsequently, taken the energy storage system charge-discharge efficiency and state of charge (SOC) into account, the rated power and capacities of each scheme was determined. Finally, based on Life Cycle Cost (LCC) theory, an energy storage system economic cost calculation model was established to compare the costs of each scheme and select the optimal one.

Lithium-ion batteries (LIBs) are extensively used in electric vehicles and portable electronics due to their high energy density. However, conventional carbonate electrolytes suffer from potential Li plating at high current density and high flammability, which hinder their fast charging capability. Herein, a multi-functional electrolyte additive, namely ethoxy(pentafluoro)cyclotriphosphazene (PFPN), is explored to realize extreme fast charging of graphite‖LiFePO4 (LFP) cells with enhanced safety. The addition of PFPN into the baseline electrolyte comprising 1 M lithium bisfluorosulfonimide salt (LiFSI) in 1,3-dioxolane not only enhances the bulk ionic conductivity but also promotes the generation of a robust and ionic-conductive solid electrolyte interphase, hence enabling superior fast-charging performance of LIBs. In addition, the PFPN additive improves the nonflammability of electrolytes and inhibits the corrosion of Al foil caused by the LiFSI salt. As a result, graphite anodes deliver reversible capacities of 314.2 mA h g−1 and 164.4 mA h g−1 at 20C and 50C, respectively. Furthermore, the graphite‖LFP full pouch cells maintain a reversible capacity of 127.9 mA h g−1 and a high capacity retention of 99.68% over 500 cycles under 1C. This work provides a viable pathway to designing fast-charging electrolytes with remarkable cycling stability and excellent flame retardancy. [ABSTRACT FROM AUTHOR]

To study the consumption of heroin in a heterogeneous environment, we propose and analyze a spatiotemporal model with a distributed delay. Using the spectral theory, we determine the basic reproduction number R0$$ {R}_0 $$, which serves a threshold role. If R0<1$$ {R}_0<1 $$, then the addiction‐free steady state is globally asymptotically stable while if R0>1$$ {R}_0>1 $$, then there is at least one addictive steady state. Moreover, when R0>1$$ {R}_0>1 $$, if one of the dispersal coefficients is zero, then there is only one addictive steady state, and it is globally asymptotically stable; if both diffusions of susceptible and addicted individuals are present, we cannot identify the temporal behavior of solutions, and hence, we study the asymptotic profile of addictive steady states when one of the dispersal coefficients tend to zero. [ABSTRACT FROM AUTHOR]

Multi-modal fusion technology gradually become a fundamental task in many fields, such as autonomous driving, smart healthcare, sentiment analysis, and human-computer interaction. It is rapidly becoming the dominant research due to its powerful perception and judgment capabilities. Under complex scenes, multi-modal fusion technology utilizes the complementary characteristics of multiple data streams to fuse different data types and achieve more accurate predictions. However, achieving outstanding performance is challenging because of equipment performance limitations, missing information, and data noise. This paper comprehensively reviews existing methods based on multi-modal fusion techniques and completes a detailed and in-depth analysis. According to the data fusion stage, multi-modal fusion has four primary methods: early fusion, deep fusion, late fusion, and hybrid fusion. The paper surveys the three major multi-modal fusion technologies that can significantly enhance the effect of data fusion and further explore the applications of multi-modal fusion technology in various fields. Finally, it discusses the challenges and explores potential research opportunities. Multi-modal tasks still need intensive study because of data heterogeneity and quality. Preserving complementary information and eliminating redundant information between modalities is critical in multi-modal technology. Invalid data fusion methods may introduce extra noise and lead to worse results. This paper provides a comprehensive and detailed summary in response to these challenges. [ABSTRACT FROM AUTHOR]