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SignificanceIt has long been hypothesized that naturally occurring electric fields (EFs) aid wound healing by guiding cell migration. Consequently, the application of EFs has significant potential for promoting wound healing. However, the mechanisms underlying the cellular response to EFs remain unclear. Recent Advances： Although the directed migration of isolated single cells under EFs has been studied for decades, only recently has experimental evidence demonstrated the distinct collective migration of large sheets of keratinocytes and corneal epithelial cells in response to applied EFs. Accumulating evidence suggests that the emergent properties of cell groups in response to EF guidance offer new opportunities for EF-assisted directional migration.Critical issuesIn this review, we provide an overview of the field of collective electrotaxis, highlighting key advances made in recent years. We also discuss advanced engineering strategies utilized to manipulate collective electrotaxis.Future directionsWe outline a series of unanswered questions in this field and propose potential applications of collective electrotaxis in developing electrical stimulation technologies for wound healing.

Background: This cross-sectional study aimed to examine the association between dietary diversity and risk of subjective cognitive decline (SCD), a precursor of dementia, in middle-aged and elderly Chinese populations residing in eastern China. Methods: Participants aged ≥ 45 years were recruited from a community in an eastern Chinese city after excluding potential objective cognitive impairment using the Mini-Cognitive Assessment Instrument (Mini-Cog). SCD was assessed using the Subjective Cognitive Decline Questionnaire-9 (SCD-Q9). Dietary data were collected using the Dietary Quality Questionnaire (DQQ), and the Food Group Diversity Score (FGDS) and the Consumed All Five Recommended Food Score (All-5) were calculated as indicators of dietary diversity. Odds ratios (ORs) and 95% confidence intervals (CIs) were computed to evaluate the associations of FGDS and All-5 scores with SCD after adjusting for age, sex, socioeconomic status, lifestyle factors, and health status. Results: Among the 871 participants, 358 (41.1%) were classified as having SCD. Compared with participants with the highest FGDS (≥8) and those with the highest All-5 score (5), those with the lowest FGDS (≤4) and the lowest All-5 score (≤3) exhibited 85% (OR = 1.85; 95% CI: 1.10–3.13; p = 0.02) and 90% (OR = 1.90; 95% CI: 1.21–2.97; p < 0.01) higher risk of SCD, respectively, after adjusting for all covariates. Fruits were the only food group among the All-5 components that demonstrated a significant association with SCD risk. Conclusions: Poor dietary diversity was associated with an elevated risk of SCD in middle-aged and older adults, and fruits were the food group with the most substantial effect. [ABSTRACT FROM AUTHOR]

With the advancement of deep learning (DL) technology, DL-based intrusion detection models have emerged as a focal point of research within the domain of cybersecurity. This paper provides an overview of the datasets frequently utilized in the research. This article presents an overview of the widely utilized datasets in the research, establishing a basis for future investigation and analysis. The text subsequently summarizes the prevalent data preprocessing methods and feature engineering techniques utilized in intrusion detection. Following this, it provides a review of seven deep learning-based intrusion detection models, namely, deep autoencoders, deep belief networks, deep neural networks, convolutional neural networks, recurrent neural networks, generative adversarial networks, and transformers. Each model is examined from various dimensions, highlighting their unique architectures and applications within the context of cybersecurity. Furthermore, this paper broadens its scope to include intrusion detection techniques facilitated by the following two large-scale predictive models: the BERT series and the GPT series. These models, leveraging the power of transformers and attention mechanisms, have demonstrated remarkable capabilities in understanding and processing sequential data. In light of these findings, this paper concludes with a prospective outlook on future research directions. Four key areas have been identified for further research. By addressing these issues and advancing research in the aforementioned areas, this paper envisions a future in which DL-based intrusion detection systems are not only more accurate and efficient but also better aligned with the dynamic and evolving landscape of cybersecurity threats. [ABSTRACT FROM AUTHOR]

Leaf chlorophyll content is an important indicator of photosynthetic capacity and health status and plays a key role in monitoring plant growth. At present, research on chlorophyll content monitoring has mainly focused on establishing a relationship between vegetation index and chlorophyll content, which was composed of several hyperspectral wavelengths, resulting in the loss of effective information contained in certain wavelengths strongly associated with disease. Therefore, it was very important to select spectral wavelengths to reveal the hyperspectral response of chlorophyll content in diseased leaves. Fifty groups of rice leaves infected with bacterial leaf blight were studied, and hyperspectral images and soil plant analysis development (SPAD) values of disease‐stressed rice leaves were collected every 5 days from the first day after inoculation. A method for predicting the SPAD value of diseased rice leaves using random forest (RF) combined with a back‐propagation optimized neural network (BPNN) was proposed. The bird swarm algorithm (BSA) and the immune algorithm (IA) were used to solve the problems of information loss, local minimization, and slow convergence of the BPNN. The BPNN, BSA–BPNN, IA–BPNN, and BSA–IA–BPNN prediction models for the SPAD value of diseased rice leaves are established. The results show that the BSA–IA–BPNN model with characteristic wavelengths selected by RF achieved the best prediction effect, and the prediction sets R2, root‐mean‐square error, and relative error are 0.97%, 4.34%, and 3.33% respectively. The shortest prediction modeling time is 1.54 s, and the model exhibits the best stability. This study provides allegorical support and a theoretical basis for achieving rapid, nondestructive detection of the SPAD value of diseased rice leaves, provides an effective reference for estimating the SPAD value of diseased rice leaves in the field, and is of great importance for rice disease monitoring. [ABSTRACT FROM AUTHOR]

The porosity of the gas diffusion layer (GDL) significantly impacts the performance of proton exchange membrane fuel cells (PEMFCs). Assembly pressure in PEMFCs leads to GDL deformation and alterations in porosity distribution. This study integrated a three‐dimensional (3D) GDL deformation model with a 3D two‐phase PEMFC model, employing a four‐term Fourier series model to optimize the fitting of the GDL porosity distribution curve. The approach quantitatively assessed the impact of GDL porosity distribution under assembly pressure on PEMFC performance. Results reveal an arched porosity distribution in GDL, peaking in the middle of low channels adjacent to ribs. High porosity enhances oxygen and heat conduction but excessive porosity may cause uneven current density distribution, hindering GDL drainage. Furthermore, the analysis compares performances at various GDL compression ratios and thicknesses, showing an initial rise then fall in current density with increasing pressure. This represents a trade‐off between the adverse impact of GDL compression on mass transfer losses and the favorable impact of reduced ohmic losses. At the optimal pressure, the current density is 3% higher than neighboring values at the same potential, and within the optimal GDL thickness range, the current density error remains below 1%. [ABSTRACT FROM AUTHOR]

Forest soil contamination is a significant problem with risks to ecosystems and human health. It reduces soil quality, hampers plant growth, and disrupts ecosystems. To understand current research and identify future directions, this study analyzed 2659 documents on forest soil contamination published on the Web of Science from 1970 to 2023. Using bibliometrics, this study systematically analyzed the knowledge structure, research hotspots, and development trends in forest soil pollution. China, the United States, and Poland were the top contributors, with 11.28%, 8.42%, and 7.15% of publications, respectively. Despite fewer publications, the Netherlands and Sweden had significant research influence. The Chinese Academy of Sciences had the most publications. The primary research topics included heavy metals, ecosystems, deposition, air pollution, and organic matter. Keyword cluster and burst analysis highlighted the importance of heavy metals, microbial communities, atmospheric deposition, and organic matter. Notably, microplastics emerged as a notable gap in the existing research by highly cited papers analysis, indicating they can be a future research focus. Overall, this paper provides a comprehensive analysis of forest soil contamination, offering insights into current research themes and emerging trends. [ABSTRACT FROM AUTHOR]