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Emerging brain-computer interface (BCI) technology holds promising potential to enhance the quality of life for individuals with disabilities. Nevertheless, the constrained accuracy of electroencephalography (EEG) signal classification poses numerous hurdles in real-world applications. In response to this predicament, we introduce a novel EEG signal classification model termed EEGGAN-Net, leveraging a data augmentation framework. By incorporating Conditional Generative Adversarial Network (CGAN) data augmentation, a cropped training strategy and a Squeeze-and-Excitation (SE) attention mechanism, EEGGAN-Net adeptly assimilates crucial features from the data, consequently enhancing classification efficacy across diverse BCI tasks. The EEGGAN-Net model exhibits notable performance metrics on the BCI Competition IV-2a and IV-2b datasets. Specifically, it achieves a classification accuracy of 81.3% with a kappa value of 0.751 on the IV-2a dataset, and a classification accuracy of 90.3% with a kappa value of 0.79 on the IV-2b dataset. Remarkably, these results surpass those of four other CNN-based decoding models. In conclusion, the amalgamation of data augmentation and attention mechanisms proves instrumental in acquiring generalized features from EEG signals, ultimately elevating the overall proficiency of EEG signal classification.

Background Rhizodeposits regulate rhizosphere interactions, processes, nutrient and energy flow, and plant-microbe communication and thus play a vital role in maintaining soil and plant health. However, it remains unclear whether and how alteration in belowground carbon allocation and chemodiversity of rhizodeposits influences microbiome functioning in the rhizosphere ecosystems. To address this research gap, we investigated the relationship of rhizosphere carbon allocation and chemodiversity with microbiome biodiversity and functioning during peanut ( Arachis hypogaea ) continuous mono-cropping. After continuously labeling plants with 13 CO 2 , we studied the chemodiversity and composition of rhizodeposits, along with the composition and diversity of active rhizosphere microbiome using metabolomic, amplicon, and shotgun metagenomic sequencing approaches based on DNA stable-isotope probing (DNA-SIP). Results Our results indicated that enrichment and depletion of rhizodeposits and active microbial taxa varied across plant growth stages and cropping durations. Specifically, a gradual decrease in the rhizosphere carbon allocation, chemodiversity, biodiversity and abundance of plant-beneficial taxa (such as Gemmatimonas , Streptomyces , Ramlibacter , and Lysobacter ), and functional gene pathways (such as quorum sensing and biosynthesis of antibiotics) was observed with years of mono-cropping. We detected significant and strong correlations between rhizodeposits and rhizosphere microbiome biodiversity and functioning, though these were regulated by different ecological processes. For instance, rhizodeposits and active bacterial communities were mainly governed by deterministic and stochastic processes, respectively. Overall, the reduction in carbon deposition and chemodiversity during peanut continuous mono-cropping tended to suppress microbial biodiversity and its functions in the rhizosphere ecosystem. Conclusions Our results, for the first time, provide the evidence underlying the mechanism of rhizosphere microbiome malfunctioning in mono-cropped systems. Our study opens new avenues to deeply disentangle the complex plant-microbe interactions from the perspective of rhizodeposits chemodiversity and composition and will serve to guide future microbiome research for improving the functioning and services of soil ecosystems. 2t8oyWpyXt9Xr4Hhf-zbjP Video abstract

In this study, we aimed to develop a deep learning model for identifying bacterial keratitis (BK) and fungal keratitis (FK) by using slit-lamp images. We retrospectively collected slit-lamp images of patients with culture-proven microbial keratitis between 1 January 2010 and 31 December 2019 from two medical centers in Taiwan. We constructed a deep learning algorithm consisting of a segmentation model for cropping cornea images and a classification model that applies different convolutional neural networks (CNNs) to differentiate between FK and BK. The CNNs included DenseNet121, DenseNet161, DenseNet169, DenseNet201, EfficientNetB3, InceptionV3, ResNet101, and ResNet50. The model performance was evaluated and presented as the area under the curve (AUC) of the receiver operating characteristic curves. A gradient-weighted class activation mapping technique was used to plot the heat map of the model. By using 1330 images from 580 patients, the deep learning algorithm achieved the highest average accuracy of 80.0%. Using different CNNs, the diagnostic accuracy for BK ranged from 79.6% to 95.9%, and that for FK ranged from 26.3% to 65.8%. The CNN of DenseNet161 showed the best model performance, with an AUC of 0.85 for both BK and FK. The heat maps revealed that the model was able to identify the corneal infiltrations. The model showed a better diagnostic accuracy than the previously reported diagnostic performance of both general ophthalmologists and corneal specialists.

The annual groundwater draft of India is the largest in the world as of 2020. The agricultural sector alone consumes about 89 per cent of groundwater draft. Besides providing assured irrigation, groundwater has significantly helped increase the cropping intensity, productivity and production of crops. But, due to the continuous exploitation of groundwater, not only has the water level been depleted but the cost of water has increased. An attempt is made in this study to find out the determinants of groundwater exploitation by taking data from 235 Indian districts drawn from different states covering two-time periods namely 1990-93 and 2017-20. The study indicated that the number of districts exploiting groundwater more than 50 per cent to its annual recharge has increased from 21 per cent in 1990-93 to 69 per cent in 2017-20. The regression analysis shows that the average size of holding is the most important factor in positively influencing groundwater exploitation, whereas the percentage of surface irrigated area to net irrigated area negatively and significantly influences the groundwater exploitation. The analysis also suggests that the impact of these two variables in determining groundwater exploitation have increased significantly over time.

Hybrid rice demonstrated superior performance in enhancing yield and efficiency in rice production compared to inbred rice. Nevertheless, the underlying mechanism responsible for the increased yield and efficiency of hybrid rice in South China's double-cropping rice region remains understudied. Field experiments over two consecutive years were conducted. Firstly, yield variations among 20 inbred and 15 hybrid rice cultivars prevalent in South China's double-cropping rice system were examined. Secondly, selecting representative hybrid and inbred rice cultivars with significant yield disparities were carried out on further analyzing dry-matter production, source-sink relationships, and nutrient absorption and utilization in both rice types. Hybrid rice displayed an average grain yield of 8.07 and 7.22 t hm in the early and late seasons, respectively, which corresponds to a 12.29% and 13.75% increase over inbred rice with statistically significant differences. In comparison to inbred rice, hybrid rice exhibited enhanced nitrogen concentration in leaves at the heading stage (15.48-16.20%), post-heading dry matter accumulation (52.62-73.21%), post-heading dry matter conversion rate (29.23-34.12%), and harvest index (17.31-18.37%). Additionally, grain nitrogen and phosphorus uptake in hybrid rice increased by 11.88-22.50% and 16.38-19.90%. Hybrid rice mainly improved post-heading nitrogen and phosphorus uptake and transport, while not total nitrogen and phosphorus uptake. Internal nitrogen and phosphorus use efficiency enhanced by 9.83%-14.31% and 10.15%-13.66%, respectively. Post-heading dry matter accumulation, harvest index, grain nitrogen and phosphorus uptake, and internal nitrogen and phosphorus use efficiency exhibited significant positive linear correlations with grain yield. The period from heading to maturity is critical for enhancing hybrid rice yield and efficiency. Improving photosynthetic capacity during this period and promoting nutrient transport to grains serve as crucial pathways for increasing grain yield and efficiency. This study is of great significance for further improvement grain yield and breeding rice cultivars with high-yield and high nutrients use efficiency for South China's double-cropped rice system.

Food availability is one of the dimensions of food security, and it is necessary to analyze the crop production scenario to estimate the availability of food in a region. Cropping sequence and cropping intensity indicate the seasonal crop production, thereby indicating the seasonal availability of food. Seasonal variation of per capita or per household availability of the cropped land determines the food security status of a given region. In Indian Sundarbans region, people’s livelihood is seriously threatened by the food insecurity. The present study aimed to determine the seasonality of cropped land as well as the cropping intensities of Gosaba CD block of Indian Sundarbans during 2017–2018, 2018–2019 and 2019–2020 cropping years using Multi-dated Sentinel-2 data. Rule-based classification was applied for cropping sequence and cropping intensity mapping. Winter season cropped land was the lowest (< 16% of the village area). The area under crop–fallow–crop sequence (200% cropping intensity) decreased, while the area under crop–fallow–fallow (100% cropping intensity) sequence increased. Area under 300% cropping intensity gradually decreased. The average cropping intensity changed from 150% in 2017–2018 to 124% and 136% in 2018–2019 and 2019–2020, respectively. Large variation of the seasonal cropped land per household was estimated, and it became the worst during winter when it became less than 0.5 bighas (0.07 ha). Crop cultivation during dry season depended on the rainfall pattern and surface water availability. The present study successfully addressed the cropping scenario and food insecurity of the study area, and hopefully, it will help the planners and policy makers to take necessary actions for cropping intensification and ensuring food security in the Indian Sundarbans region.

Many forms of air pollution increase as science and technology rapidly advance. In particular, fine dust harms the human body, causing or worsening heart and lung-related diseases. In this study, the level of fine dust in Seoul after 8 h is predicted to prevent health damage in advance. We construct a dataset by combining two modalities (i.e., numerical and image data) for accurate prediction. In addition, we propose a multimodal deep learning model combining a Long Short Term Memory (LSTM) and Convolutional Neural Network (CNN). An LSTM AutoEncoder is chosen as a model for numerical time series data processing and basic CNN. A Visual Geometry Group Neural Network (VGGNet) (VGG16, VGG19) is also chosen as a CNN model for image processing to compare performance differences according to network depth. The VGGNet is a standard deep CNN architecture with multiple layers. Our multimodal deep learning model using two modalities (i.e., numerical and image data) showed better performance than a single deep learning model using only one modality (numerical data). Specifically, the performance improved up to 14.16% when the VGG19 model, which has a deeper network, was used rather than the VGG16 model.

Most traditional injection-production optimization methods that treat the entire oil reservoir as a whole require re-optimization when facing new reservoirs, which is not only time-consuming but also does not make full use of historical experience information. This study decomposes the reservoir into independent basic production units to increase sample size and diversity and utilizes image enhancement techniques to augment the number of samples. Two frameworks based on convolutional neural networks (CNNs) are employed to recommend optimal control strategies for inputted well groups. Framework 1 uses bottom hole pressure (BHP) as a control variable and trains a CNN with optimal BHP obtained by reinforcement learning algorithms as labels. Framework 2 saves BHP and corresponding oil well revenue (NPV) during reinforcement learning optimization and trains a CNN with well groups and BHP as features and NPV as labels. The CNN in this framework is capable of directly outputting the NPV according to control strategies. The particle swarm algorithm (PSO) is used to generate control strategies and call CNN to predict development effects until PSO converges to the optimal production strategy. The experimental results demonstrate that the CNN-based frameworks outperform the traditional PSO-based methods in terms of accuracy and computational efficiency. Framework 1 achieves an output accuracy of 87% for predicting the optimal BHP for new well groups, while Framework 2 achieves an accuracy of 78%. Both frameworks exhibit fast running times, with each iteration taking less than 1 s. This study provides a more effective and accurate method for optimizing oil well production in oil reservoirs by decomposing oil reservoirs into independent units and using CNN to construct an algorithm framework, which is of great significance for the real-time optimization and control of oil wells in oil fields.

The discord between the nitrogen (N) fertilizer use and the actual N requirement in Indian agriculture is of enormous concern. When N is overused, it emerges as a threat to the environment, and crop yields are affected when it is underused. Nutrient budgeting is a useful tool in assessing the inflows and outflows of nutrients to the agricultural system and formulating future strategies. We constructed a nitrogen budget for Indian agriculture for 1961–2017. The N input to Indian croplands increased from 4.87 million tons (Mt) to 24.08 Mt during this period. Among the different components of N use in 2017, the contribution of fertilizer is the highest (70%), followed by biological N fixation (16%), manure (9%), and atmospheric deposition (4%). The analysis portrayed Indian agriculture’s transformation from the N deficit value of − 0.61 Mt in 1961 to a surplus-value of 1.21 Mt as of 2017. The crop N use efficiency during the period decreased from 72 to 55%. Since the policies and socio-economic factors are the commonly studied drivers of N fertilizer use, crop production factors have not received due attention. We dissect the contribution of these factors to N fertilizer use. The fertilizer application rate (FAR) is the most important among the major crop production factors that drive N fertilizer use. Our findings propose that the surplus N in Indian agriculture, hastened by higher FAR, may pose serious sustainability issues if not addressed.